Publish 11 evaluated Astra histories with reproducible checkpoint artifacts (part 17)

.gitattributes

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+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_entr(const at::Tensor & self);
+TORCH_API at::Tensor & special_entr_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_entr_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at

shared/package-files/31/31c4850d453892009abd4d6edfebaa04697351988b1b1339a8f7c11603d3a9d1/blob

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+from numpy.testing import assert_allclose
+from scipy.linalg import cython_lapack as cython_lapack
+from scipy.linalg import lapack
+
+
+class TestLamch:
+
+    def test_slamch(self):
+        for c in [b'e', b's', b'b', b'p', b'n', b'r', b'm', b'u', b'l', b'o']:
+            assert_allclose(cython_lapack._test_slamch(c),
+                            lapack.slamch(c))
+
+    def test_dlamch(self):
+        for c in [b'e', b's', b'b', b'p', b'n', b'r', b'm', b'u', b'l', b'o']:
+            assert_allclose(cython_lapack._test_dlamch(c),
+                            lapack.dlamch(c))
+
+    def test_complex_ladiv(self):
+        cx = .5 + 1.j
+        cy = .875 + 2.j
+        assert_allclose(cython_lapack._test_zladiv(cy, cx), 1.95+0.1j)
+        assert_allclose(cython_lapack._test_cladiv(cy, cx), 1.95+0.1j)

shared/package-files/31/31d1e0b9b3bb131716829f12f823fd07851f27f6912e02dab74cb3d295d011d8/blob

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+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+/// \brief Implementation of Flight RPC client.
+
+#pragma once
+
+#include <chrono>
+#include <memory>
+#include <string>
+#include <utility>
+#include <variant>
+#include <vector>
+
+#include "arrow/ipc/options.h"
+#include "arrow/ipc/reader.h"
+#include "arrow/ipc/writer.h"
+#include "arrow/result.h"
+#include "arrow/status.h"
+#include "arrow/util/cancel.h"
+
+#include "arrow/flight/type_fwd.h"
+#include "arrow/flight/types.h"  // IWYU pragma: keep
+#include "arrow/flight/visibility.h"
+
+namespace arrow {
+
+class RecordBatch;
+class Schema;
+
+namespace flight {
+
+/// \brief A duration type for Flight call timeouts.
+typedef std::chrono::duration<double, std::chrono::seconds::period> TimeoutDuration;
+
+/// \brief Hints to the underlying RPC layer for Arrow Flight calls.
+class ARROW_FLIGHT_EXPORT FlightCallOptions {
+ public:
+  /// Create a default set of call options.
+  FlightCallOptions();
+
+  /// \brief An optional timeout for this call. Negative durations
+  /// mean an implementation-defined default behavior will be used
+  /// instead. This is the default value.
+  TimeoutDuration timeout;
+
+  /// \brief IPC reader options, if applicable for the call.
+  ipc::IpcReadOptions read_options;
+
+  /// \brief IPC writer options, if applicable for the call.
+  ipc::IpcWriteOptions write_options;
+
+  /// \brief Headers for client to add to context.
+  std::vector<std::pair<std::string, std::string>> headers;
+
+  /// \brief A token to enable interactive user cancellation of long-running requests.
+  StopToken stop_token;
+
+  /// \brief An optional memory manager to control where to allocate incoming data.
+  std::shared_ptr<MemoryManager> memory_manager;
+};
+
+/// \brief Indicate that the client attempted to write a message
+///     larger than the soft limit set via write_size_limit_bytes.
+class ARROW_FLIGHT_EXPORT FlightWriteSizeStatusDetail : public arrow::StatusDetail {
+ public:
+  explicit FlightWriteSizeStatusDetail(int64_t limit, int64_t actual)
+      : limit_(limit), actual_(actual) {}
+  const char* type_id() const override;
+  std::string ToString() const override;
+  int64_t limit() const { return limit_; }
+  int64_t actual() const { return actual_; }
+
+  /// \brief Extract this status detail from a status, or return
+  ///     nullptr if the status doesn't contain this status detail.
+  static std::shared_ptr<FlightWriteSizeStatusDetail> UnwrapStatus(
+      const arrow::Status& status);
+
+ private:
+  int64_t limit_;
+  int64_t actual_;
+};
+
+struct ARROW_FLIGHT_EXPORT FlightClientOptions {
+  /// \brief Root certificates to use for validating server
+  /// certificates.
+  std::string tls_root_certs;
+  /// \brief Override the hostname checked by TLS. Use with caution.
+  std::string override_hostname;
+  /// \brief The client certificate to use if using Mutual TLS
+  std::string cert_chain;
+  /// \brief The private key associated with the client certificate for Mutual TLS
+  std::string private_key;
+  /// \brief A list of client middleware to apply.
+  std::vector<std::shared_ptr<ClientMiddlewareFactory>> middleware;
+  /// \brief A soft limit on the number of bytes to write in a single
+  ///     batch when sending Arrow data to a server.
+  ///
+  /// Used to help limit server memory consumption. Only enabled if
+  /// positive. When enabled, FlightStreamWriter.Write* may yield a
+  /// IOError with error detail FlightWriteSizeStatusDetail.
+  int64_t write_size_limit_bytes = 0;
+
+  /// \brief Generic connection options, passed to the underlying
+  ///     transport; interpretation is implementation-dependent.
+  std::vector<std::pair<std::string, std::variant<int, std::string>>> generic_options;
+
+  /// \brief Use TLS without validating the server certificate. Use with caution.
+  bool disable_server_verification = false;
+
+  /// \brief Get default options.
+  static FlightClientOptions Defaults();
+};
+
+/// \brief A RecordBatchReader exposing Flight metadata and cancel
+/// operations.
+class ARROW_FLIGHT_EXPORT FlightStreamReader : public MetadataRecordBatchReader {
+ public:
+  /// \brief Try to cancel the call.
+  virtual void Cancel() = 0;
+
+  using MetadataRecordBatchReader::ToRecordBatches;
+  /// \brief Consume entire stream as a vector of record batches
+  virtual arrow::Result<std::vector<std::shared_ptr<RecordBatch>>> ToRecordBatches(
+      const StopToken& stop_token) = 0;
+
+  using MetadataRecordBatchReader::ToTable;
+  /// \brief Consume entire stream as a Table
+  arrow::Result<std::shared_ptr<Table>> ToTable(const StopToken& stop_token);
+
+  using MetadataRecordBatchReader::stats;
+  /// \brief Return current read statistics
+  virtual arrow::ipc::ReadStats stats() const = 0;
+};
+
+// Silence warning
+// "non dll-interface class RecordBatchReader used as base for dll-interface class"
+#ifdef _MSC_VER
+#  pragma warning(push)
+#  pragma warning(disable : 4275)
+#endif
+
+/// \brief A RecordBatchWriter that also allows sending
+/// application-defined metadata via the Flight protocol.
+class ARROW_FLIGHT_EXPORT FlightStreamWriter : public MetadataRecordBatchWriter {
+ public:
+  /// \brief Indicate that the application is done writing to this stream.
+  ///
+  /// The application may not write to this stream after calling
+  /// this. This differs from closing the stream because this writer
+  /// may represent only one half of a readable and writable stream.
+  virtual Status DoneWriting() = 0;
+};
+
+#ifdef _MSC_VER
+#  pragma warning(pop)
+#endif
+
+/// \brief A reader for application-specific metadata sent back to the
+/// client during an upload.
+class ARROW_FLIGHT_EXPORT FlightMetadataReader {
+ public:
+  virtual ~FlightMetadataReader();
+  /// \brief Read a message from the server.
+  virtual Status ReadMetadata(std::shared_ptr<Buffer>* out) = 0;
+};
+
+/// \brief Client class for Arrow Flight RPC services.
+class ARROW_FLIGHT_EXPORT FlightClient {
+ public:
+  ~FlightClient();
+
+  /// \brief Connect to an unauthenticated flight service
+  /// \param[in] location the URI
+  /// \return Arrow result with the created FlightClient, OK status may not indicate that
+  /// the connection was successful
+  static arrow::Result<std::unique_ptr<FlightClient>> Connect(const Location& location);
+
+  /// \brief Connect to an unauthenticated flight service
+  /// \param[in] location the URI
+  /// \param[in] options Other options for setting up the client
+  /// \return Arrow result with the created FlightClient, OK status may not indicate that
+  /// the connection was successful
+  static arrow::Result<std::unique_ptr<FlightClient>> Connect(
+      const Location& location, const FlightClientOptions& options);
+
+  /// \brief Authenticate to the server using the given handler.
+  /// \param[in] options Per-RPC options
+  /// \param[in] auth_handler The authentication mechanism to use
+  /// \return Status OK if the client authenticated successfully
+  Status Authenticate(const FlightCallOptions& options,
+                      std::unique_ptr<ClientAuthHandler> auth_handler);
+
+  /// \brief Authenticate to the server using basic HTTP style authentication.
+  /// \param[in] options Per-RPC options
+  /// \param[in] username Username to use
+  /// \param[in] password Password to use
+  /// \return Arrow result with bearer token and status OK if client authenticated
+  /// successfully
+  arrow::Result<std::pair<std::string, std::string>> AuthenticateBasicToken(
+      const FlightCallOptions& options, const std::string& username,
+      const std::string& password);
+
+  /// \brief Perform the indicated action, returning an iterator to the stream
+  /// of results, if any
+  /// \param[in] options Per-RPC options
+  /// \param[in] action the action to be performed
+  /// \return Arrow result with an iterator object for reading the returned results
+  arrow::Result<std::unique_ptr<ResultStream>> DoAction(const FlightCallOptions& options,
+                                                        const Action& action);
+  arrow::Result<std::unique_ptr<ResultStream>> DoAction(const Action& action) {
+    return DoAction({}, action);
+  }
+
+  /// \brief Perform the CancelFlightInfo action, returning a
+  /// CancelFlightInfoResult
+  ///
+  /// \param[in] options Per-RPC options
+  /// \param[in] request The CancelFlightInfoRequest
+  /// \return Arrow result with a CancelFlightInfoResult
+  arrow::Result<CancelFlightInfoResult> CancelFlightInfo(
+      const FlightCallOptions& options, const CancelFlightInfoRequest& request);
+  arrow::Result<CancelFlightInfoResult> CancelFlightInfo(
+      const CancelFlightInfoRequest& request) {
+    return CancelFlightInfo({}, request);
+  }
+
+  /// \brief Perform the RenewFlightEndpoint action, returning a renewed
+  /// FlightEndpoint
+  ///
+  /// \param[in] options Per-RPC options
+  /// \param[in] request The RenewFlightEndpointRequest
+  /// \return Arrow result with a renewed FlightEndpoint
+  arrow::Result<FlightEndpoint> RenewFlightEndpoint(
+      const FlightCallOptions& options, const RenewFlightEndpointRequest& request);
+  arrow::Result<FlightEndpoint> RenewFlightEndpoint(
+      const RenewFlightEndpointRequest& request) {
+    return RenewFlightEndpoint({}, request);
+  }
+
+  /// \brief Retrieve a list of available Action types
+  /// \param[in] options Per-RPC options
+  /// \return Arrow result with the available actions
+  arrow::Result<std::vector<ActionType>> ListActions(const FlightCallOptions& options);
+  arrow::Result<std::vector<ActionType>> ListActions() {
+    return ListActions(FlightCallOptions());
+  }
+
+  /// \brief Request access plan for a single flight, which may be an existing
+  /// dataset or a command to be executed
+  /// \param[in] options Per-RPC options
+  /// \param[in] descriptor the dataset request, whether a named dataset or
+  /// command
+  /// \return Arrow result with the FlightInfo describing where to access the dataset
+  arrow::Result<std::unique_ptr<FlightInfo>> GetFlightInfo(
+      const FlightCallOptions& options, const FlightDescriptor& descriptor);
+  arrow::Result<std::unique_ptr<FlightInfo>> GetFlightInfo(
+      const FlightDescriptor& descriptor) {
+    return GetFlightInfo({}, descriptor);
+  }
+
+  /// \brief Asynchronous GetFlightInfo.
+  /// \param[in] options Per-RPC options
+  /// \param[in] descriptor the dataset request
+  /// \param[in] listener Callbacks for response and RPC completion
+  void GetFlightInfoAsync(const FlightCallOptions& options,
+                          const FlightDescriptor& descriptor,
+                          std::shared_ptr<AsyncListener<FlightInfo>> listener);
+  void GetFlightInfoAsync(const FlightDescriptor& descriptor,
+                          std::shared_ptr<AsyncListener<FlightInfo>> listener) {
+    return GetFlightInfoAsync({}, descriptor, std::move(listener));
+  }
+
+  /// \brief Asynchronous GetFlightInfo returning a Future.
+  /// \param[in] options Per-RPC options
+  /// \param[in] descriptor the dataset request
+  arrow::Future<FlightInfo> GetFlightInfoAsync(const FlightCallOptions& options,
+                                               const FlightDescriptor& descriptor);
+  arrow::Future<FlightInfo> GetFlightInfoAsync(const FlightDescriptor& descriptor) {
+    return GetFlightInfoAsync({}, descriptor);
+  }
+
+  /// \brief Request and poll a long running query
+  /// \param[in] options Per-RPC options
+  /// \param[in] descriptor the dataset request or a descriptor returned by a
+  /// prior PollFlightInfo call
+  /// \return Arrow result with the PollInfo describing the status of
+  /// the requested query
+  arrow::Result<std::unique_ptr<PollInfo>> PollFlightInfo(
+      const FlightCallOptions& options, const FlightDescriptor& descriptor);
+  arrow::Result<std::unique_ptr<PollInfo>> PollFlightInfo(
+      const FlightDescriptor& descriptor) {
+    return PollFlightInfo({}, descriptor);
+  }
+
+  /// \brief Request schema for a single flight, which may be an existing
+  /// dataset or a command to be executed
+  /// \param[in] options Per-RPC options
+  /// \param[in] descriptor the dataset request, whether a named dataset or
+  /// command
+  /// \return Arrow result with the SchemaResult describing the dataset schema
+  arrow::Result<std::unique_ptr<SchemaResult>> GetSchema(
+      const FlightCallOptions& options, const FlightDescriptor& descriptor);
+
+  arrow::Result<std::unique_ptr<SchemaResult>> GetSchema(
+      const FlightDescriptor& descriptor) {
+    return GetSchema({}, descriptor);
+  }
+
+  /// \brief List all available flights known to the server
+  /// \return Arrow result with an iterator that returns a FlightInfo for each flight
+  arrow::Result<std::unique_ptr<FlightListing>> ListFlights();
+
+  /// \brief List available flights given indicated filter criteria
+  /// \param[in] options Per-RPC options
+  /// \param[in] criteria the filter criteria (opaque)
+  /// \return Arrow result with an iterator that returns a FlightInfo for each flight
+  arrow::Result<std::unique_ptr<FlightListing>> ListFlights(
+      const FlightCallOptions& options, const Criteria& criteria);
+
+  /// \brief Given a flight ticket and schema, request to be sent the
+  /// stream. Returns record batch stream reader
+  /// \param[in] options Per-RPC options
+  /// \param[in] ticket The flight ticket to use
+  /// \return Arrow result with the returned RecordBatchReader
+  arrow::Result<std::unique_ptr<FlightStreamReader>> DoGet(
+      const FlightCallOptions& options, const Ticket& ticket);
+  arrow::Result<std::unique_ptr<FlightStreamReader>> DoGet(const Ticket& ticket) {
+    return DoGet({}, ticket);
+  }
+
+  /// \brief DoPut return value
+  struct DoPutResult {
+    /// \brief a writer to write record batches to
+    std::unique_ptr<FlightStreamWriter> writer;
+    /// \brief a reader for application metadata from the server
+    std::unique_ptr<FlightMetadataReader> reader;
+  };
+  /// \brief Upload data to a Flight described by the given
+  /// descriptor. The caller must call Close() on the returned stream
+  /// once they are done writing.
+  ///
+  /// The reader and writer are linked; closing the writer will also
+  /// close the reader. Use \a DoneWriting to only close the write
+  /// side of the channel.
+  ///
+  /// \param[in] options Per-RPC options
+  /// \param[in] descriptor the descriptor of the stream
+  /// \param[in] schema the schema for the data to upload
+  /// \return Arrow result with a DoPutResult struct holding a reader and a writer
+  arrow::Result<DoPutResult> DoPut(const FlightCallOptions& options,
+                                   const FlightDescriptor& descriptor,
+                                   const std::shared_ptr<Schema>& schema);
+
+  arrow::Result<DoPutResult> DoPut(const FlightDescriptor& descriptor,
+                                   const std::shared_ptr<Schema>& schema) {
+    return DoPut({}, descriptor, schema);
+  }
+
+  struct DoExchangeResult {
+    std::unique_ptr<FlightStreamWriter> writer;
+    std::unique_ptr<FlightStreamReader> reader;
+  };
+  arrow::Result<DoExchangeResult> DoExchange(const FlightCallOptions& options,
+                                             const FlightDescriptor& descriptor);
+  arrow::Result<DoExchangeResult> DoExchange(const FlightDescriptor& descriptor) {
+    return DoExchange({}, descriptor);
+  }
+
+  /// \brief Set server session option(s) by name/value. Sessions are generally
+  /// persisted via HTTP cookies.
+  /// \param[in] options Per-RPC options
+  /// \param[in] request The server session options to set
+  ::arrow::Result<SetSessionOptionsResult> SetSessionOptions(
+      const FlightCallOptions& options, const SetSessionOptionsRequest& request);
+
+  /// \brief Get the current server session options. The session is generally
+  /// accessed via an HTTP cookie.
+  /// \param[in] options Per-RPC options
+  /// \param[in] request The (empty) GetSessionOptions request object.
+  ::arrow::Result<GetSessionOptionsResult> GetSessionOptions(
+      const FlightCallOptions& options, const GetSessionOptionsRequest& request);
+
+  /// \brief Close/invalidate the current server session. The session is generally
+  /// accessed via an HTTP cookie.
+  /// \param[in] options Per-RPC options
+  /// \param[in] request The (empty) CloseSession request object.
+  ::arrow::Result<CloseSessionResult> CloseSession(const FlightCallOptions& options,
+                                                   const CloseSessionRequest& request);
+
+  /// \brief Explicitly shut down and clean up the client.
+  ///
+  /// For backwards compatibility, this will be implicitly called by
+  /// the destructor if not already called, but this gives the
+  /// application no chance to handle errors, so it is recommended to
+  /// explicitly close the client.
+  ///
+  /// \since 8.0.0
+  Status Close();
+
+  /// \brief Whether this client supports asynchronous methods.
+  bool supports_async() const;
+
+  /// \brief Check whether this client supports asynchronous methods.
+  ///
+  /// This is like supports_async(), except that a detailed error message
+  /// is returned if async support is not available.  If async support is
+  /// available, this function returns successfully.
+  Status CheckAsyncSupport() const;
+
+ private:
+  FlightClient();
+  Status CheckOpen() const;
+  std::unique_ptr<internal::ClientTransport> transport_;
+  bool closed_;
+  int64_t write_size_limit_bytes_;
+};
+
+}  // namespace flight
+}  // namespace arrow

shared/package-files/31/31d3fcccd95241e794ee9b0e5f0194750d7ef172b360297998a30872930288df/blob

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+"""
+datetimelike delegation
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_integer_dtype,
+    is_list_like,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    CategoricalDtype,
+    DatetimeTZDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import ABCSeries
+
+from pandas.core.accessor import (
+    PandasDelegate,
+    delegate_names,
+)
+from pandas.core.arrays import (
+    DatetimeArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.arrow.array import ArrowExtensionArray
+from pandas.core.base import (
+    NoNewAttributesMixin,
+    PandasObject,
+)
+from pandas.core.indexes.datetimes import DatetimeIndex
+from pandas.core.indexes.timedeltas import TimedeltaIndex
+
+if TYPE_CHECKING:
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+class Properties(PandasDelegate, PandasObject, NoNewAttributesMixin):
+    _hidden_attrs = PandasObject._hidden_attrs | {
+        "orig",
+        "name",
+    }
+
+    def __init__(self, data: Series, orig) -> None:
+        if not isinstance(data, ABCSeries):
+            raise TypeError(
+                f"cannot convert an object of type {type(data)} to a datetimelike index"
+            )
+
+        self._parent = data
+        self.orig = orig
+        self.name = getattr(data, "name", None)
+        self._freeze()
+
+    def _get_values(self):
+        data = self._parent
+        if lib.is_np_dtype(data.dtype, "M"):
+            return DatetimeIndex(data, copy=False, name=self.name)
+
+        elif isinstance(data.dtype, DatetimeTZDtype):
+            return DatetimeIndex(data, copy=False, name=self.name)
+
+        elif lib.is_np_dtype(data.dtype, "m"):
+            return TimedeltaIndex(data, copy=False, name=self.name)
+
+        elif isinstance(data.dtype, PeriodDtype):
+            return PeriodArray(data, copy=False)
+
+        raise TypeError(
+            f"cannot convert an object of type {type(data)} to a datetimelike index"
+        )
+
+    def _delegate_property_get(self, name: str):
+        from pandas import Series
+
+        values = self._get_values()
+
+        result = getattr(values, name)
+
+        # maybe need to upcast (ints)
+        if isinstance(result, np.ndarray):
+            if is_integer_dtype(result):
+                result = result.astype("int64")
+        elif not is_list_like(result):
+            return result
+
+        result = np.asarray(result)
+
+        if self.orig is not None:
+            index = self.orig.index
+        else:
+            index = self._parent.index
+        # return the result as a Series
+        result = Series(result, index=index, name=self.name).__finalize__(self._parent)
+
+        # setting this object will show a SettingWithCopyWarning/Error
+        result._is_copy = (
+            "modifications to a property of a datetimelike "
+            "object are not supported and are discarded. "
+            "Change values on the original."
+        )
+
+        return result
+
+    def _delegate_property_set(self, name: str, value, *args, **kwargs):
+        raise ValueError(
+            "modifications to a property of a datetimelike object are not supported. "
+            "Change values on the original."
+        )
+
+    def _delegate_method(self, name: str, *args, **kwargs):
+        from pandas import Series
+
+        values = self._get_values()
+
+        method = getattr(values, name)
+        result = method(*args, **kwargs)
+
+        if not is_list_like(result):
+            return result
+
+        result = Series(result, index=self._parent.index, name=self.name).__finalize__(
+            self._parent
+        )
+
+        # setting this object will show a SettingWithCopyWarning/Error
+        result._is_copy = (
+            "modifications to a method of a datetimelike "
+            "object are not supported and are discarded. "
+            "Change values on the original."
+        )
+
+        return result
+
+
+@delegate_names(
+    delegate=ArrowExtensionArray,
+    accessors=TimedeltaArray._datetimelike_ops,
+    typ="property",
+    accessor_mapping=lambda x: f"_dt_{x}",
+    raise_on_missing=False,
+)
+@delegate_names(
+    delegate=ArrowExtensionArray,
+    accessors=TimedeltaArray._datetimelike_methods,
+    typ="method",
+    accessor_mapping=lambda x: f"_dt_{x}",
+    raise_on_missing=False,
+)
+@delegate_names(
+    delegate=ArrowExtensionArray,
+    accessors=DatetimeArray._datetimelike_ops,
+    typ="property",
+    accessor_mapping=lambda x: f"_dt_{x}",
+    raise_on_missing=False,
+)
+@delegate_names(
+    delegate=ArrowExtensionArray,
+    accessors=DatetimeArray._datetimelike_methods,
+    typ="method",
+    accessor_mapping=lambda x: f"_dt_{x}",
+    raise_on_missing=False,
+)
+class ArrowTemporalProperties(PandasDelegate, PandasObject, NoNewAttributesMixin):
+    def __init__(self, data: Series, orig) -> None:
+        if not isinstance(data, ABCSeries):
+            raise TypeError(
+                f"cannot convert an object of type {type(data)} to a datetimelike index"
+            )
+
+        self._parent = data
+        self._orig = orig
+        self._freeze()
+
+    def _delegate_property_get(self, name: str):
+        if not hasattr(self._parent.array, f"_dt_{name}"):
+            raise NotImplementedError(
+                f"dt.{name} is not supported for {self._parent.dtype}"
+            )
+        result = getattr(self._parent.array, f"_dt_{name}")
+
+        if not is_list_like(result):
+            return result
+
+        if self._orig is not None:
+            index = self._orig.index
+        else:
+            index = self._parent.index
+        # return the result as a Series, which is by definition a copy
+        result = type(self._parent)(
+            result, index=index, name=self._parent.name
+        ).__finalize__(self._parent)
+
+        return result
+
+    def _delegate_method(self, name: str, *args, **kwargs):
+        if not hasattr(self._parent.array, f"_dt_{name}"):
+            raise NotImplementedError(
+                f"dt.{name} is not supported for {self._parent.dtype}"
+            )
+
+        result = getattr(self._parent.array, f"_dt_{name}")(*args, **kwargs)
+
+        if self._orig is not None:
+            index = self._orig.index
+        else:
+            index = self._parent.index
+        # return the result as a Series, which is by definition a copy
+        result = type(self._parent)(
+            result, index=index, name=self._parent.name
+        ).__finalize__(self._parent)
+
+        return result
+
+    def to_pytimedelta(self):
+        return cast(ArrowExtensionArray, self._parent.array)._dt_to_pytimedelta()
+
+    def to_pydatetime(self):
+        # GH#20306
+        warnings.warn(
+            f"The behavior of {type(self).__name__}.to_pydatetime is deprecated, "
+            "in a future version this will return a Series containing python "
+            "datetime objects instead of an ndarray. To retain the old behavior, "
+            "call `np.array` on the result",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return cast(ArrowExtensionArray, self._parent.array)._dt_to_pydatetime()
+
+    def isocalendar(self) -> DataFrame:
+        from pandas import DataFrame
+
+        result = (
+            cast(ArrowExtensionArray, self._parent.array)
+            ._dt_isocalendar()
+            ._pa_array.combine_chunks()
+        )
+        iso_calendar_df = DataFrame(
+            {
+                col: type(self._parent.array)(result.field(i))  # type: ignore[call-arg]
+                for i, col in enumerate(["year", "week", "day"])
+            }
+        )
+        return iso_calendar_df
+
+    @property
+    def components(self) -> DataFrame:
+        from pandas import DataFrame
+
+        components_df = DataFrame(
+            {
+                col: getattr(self._parent.array, f"_dt_{col}")
+                for col in [
+                    "days",
+                    "hours",
+                    "minutes",
+                    "seconds",
+                    "milliseconds",
+                    "microseconds",
+                    "nanoseconds",
+                ]
+            }
+        )
+        return components_df
+
+
+@delegate_names(
+    delegate=DatetimeArray,
+    accessors=DatetimeArray._datetimelike_ops + ["unit"],
+    typ="property",
+)
+@delegate_names(
+    delegate=DatetimeArray,
+    accessors=DatetimeArray._datetimelike_methods + ["as_unit"],
+    typ="method",
+)
+class DatetimeProperties(Properties):
+    """
+    Accessor object for datetimelike properties of the Series values.
+
+    Examples
+    --------
+    >>> seconds_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="s"))
+    >>> seconds_series
+    0   2000-01-01 00:00:00
+    1   2000-01-01 00:00:01
+    2   2000-01-01 00:00:02
+    dtype: datetime64[ns]
+    >>> seconds_series.dt.second
+    0    0
+    1    1
+    2    2
+    dtype: int32
+
+    >>> hours_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="h"))
+    >>> hours_series
+    0   2000-01-01 00:00:00
+    1   2000-01-01 01:00:00
+    2   2000-01-01 02:00:00
+    dtype: datetime64[ns]
+    >>> hours_series.dt.hour
+    0    0
+    1    1
+    2    2
+    dtype: int32
+
+    >>> quarters_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="QE"))
+    >>> quarters_series
+    0   2000-03-31
+    1   2000-06-30
+    2   2000-09-30
+    dtype: datetime64[ns]
+    >>> quarters_series.dt.quarter
+    0    1
+    1    2
+    2    3
+    dtype: int32
+
+    Returns a Series indexed like the original Series.
+    Raises TypeError if the Series does not contain datetimelike values.
+    """
+
+    def to_pydatetime(self) -> np.ndarray:
+        """
+        Return the data as an array of :class:`datetime.datetime` objects.
+
+        .. deprecated:: 2.1.0
+
+            The current behavior of dt.to_pydatetime is deprecated.
+            In a future version this will return a Series containing python
+            datetime objects instead of a ndarray.
+
+        Timezone information is retained if present.
+
+        .. warning::
+
+           Python's datetime uses microsecond resolution, which is lower than
+           pandas (nanosecond). The values are truncated.
+
+        Returns
+        -------
+        numpy.ndarray
+            Object dtype array containing native Python datetime objects.
+
+        See Also
+        --------
+        datetime.datetime : Standard library value for a datetime.
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.date_range('20180310', periods=2))
+        >>> s
+        0   2018-03-10
+        1   2018-03-11
+        dtype: datetime64[ns]
+
+        >>> s.dt.to_pydatetime()
+        array([datetime.datetime(2018, 3, 10, 0, 0),
+               datetime.datetime(2018, 3, 11, 0, 0)], dtype=object)
+
+        pandas' nanosecond precision is truncated to microseconds.
+
+        >>> s = pd.Series(pd.date_range('20180310', periods=2, freq='ns'))
+        >>> s
+        0   2018-03-10 00:00:00.000000000
+        1   2018-03-10 00:00:00.000000001
+        dtype: datetime64[ns]
+
+        >>> s.dt.to_pydatetime()
+        array([datetime.datetime(2018, 3, 10, 0, 0),
+               datetime.datetime(2018, 3, 10, 0, 0)], dtype=object)
+        """
+        # GH#20306
+        warnings.warn(
+            f"The behavior of {type(self).__name__}.to_pydatetime is deprecated, "
+            "in a future version this will return a Series containing python "
+            "datetime objects instead of an ndarray. To retain the old behavior, "
+            "call `np.array` on the result",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self._get_values().to_pydatetime()
+
+    @property
+    def freq(self):
+        return self._get_values().inferred_freq
+
+    def isocalendar(self) -> DataFrame:
+        """
+        Calculate year, week, and day according to the ISO 8601 standard.
+
+        Returns
+        -------
+        DataFrame
+            With columns year, week and day.
+
+        See Also
+        --------
+        Timestamp.isocalendar : Function return a 3-tuple containing ISO year,
+            week number, and weekday for the given Timestamp object.
+        datetime.date.isocalendar : Return a named tuple object with
+            three components: year, week and weekday.
+
+        Examples
+        --------
+        >>> ser = pd.to_datetime(pd.Series(["2010-01-01", pd.NaT]))
+        >>> ser.dt.isocalendar()
+           year  week  day
+        0  2009    53     5
+        1  <NA>  <NA>  <NA>
+        >>> ser.dt.isocalendar().week
+        0      53
+        1    <NA>
+        Name: week, dtype: UInt32
+        """
+        return self._get_values().isocalendar().set_index(self._parent.index)
+
+
+@delegate_names(
+    delegate=TimedeltaArray, accessors=TimedeltaArray._datetimelike_ops, typ="property"
+)
+@delegate_names(
+    delegate=TimedeltaArray,
+    accessors=TimedeltaArray._datetimelike_methods,
+    typ="method",
+)
+class TimedeltaProperties(Properties):
+    """
+    Accessor object for datetimelike properties of the Series values.
+
+    Returns a Series indexed like the original Series.
+    Raises TypeError if the Series does not contain datetimelike values.
+
+    Examples
+    --------
+    >>> seconds_series = pd.Series(
+    ...     pd.timedelta_range(start="1 second", periods=3, freq="s")
+    ... )
+    >>> seconds_series
+    0   0 days 00:00:01
+    1   0 days 00:00:02
+    2   0 days 00:00:03
+    dtype: timedelta64[ns]
+    >>> seconds_series.dt.seconds
+    0    1
+    1    2
+    2    3
+    dtype: int32
+    """
+
+    def to_pytimedelta(self) -> np.ndarray:
+        """
+        Return an array of native :class:`datetime.timedelta` objects.
+
+        Python's standard `datetime` library uses a different representation
+        timedelta's. This method converts a Series of pandas Timedeltas
+        to `datetime.timedelta` format with the same length as the original
+        Series.
+
+        Returns
+        -------
+        numpy.ndarray
+            Array of 1D containing data with `datetime.timedelta` type.
+
+        See Also
+        --------
+        datetime.timedelta : A duration expressing the difference
+            between two date, time, or datetime.
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit="d"))
+        >>> s
+        0   0 days
+        1   1 days
+        2   2 days
+        3   3 days
+        4   4 days
+        dtype: timedelta64[ns]
+
+        >>> s.dt.to_pytimedelta()
+        array([datetime.timedelta(0), datetime.timedelta(days=1),
+        datetime.timedelta(days=2), datetime.timedelta(days=3),
+        datetime.timedelta(days=4)], dtype=object)
+        """
+        return self._get_values().to_pytimedelta()
+
+    @property
+    def components(self):
+        """
+        Return a Dataframe of the components of the Timedeltas.
+
+        Returns
+        -------
+        DataFrame
+
+        Examples
+        --------
+        >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='s'))
+        >>> s
+        0   0 days 00:00:00
+        1   0 days 00:00:01
+        2   0 days 00:00:02
+        3   0 days 00:00:03
+        4   0 days 00:00:04
+        dtype: timedelta64[ns]
+        >>> s.dt.components
+           days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
+        0     0      0        0        0             0             0            0
+        1     0      0        0        1             0             0            0
+        2     0      0        0        2             0             0            0
+        3     0      0        0        3             0             0            0
+        4     0      0        0        4             0             0            0
+        """
+        return (
+            self._get_values()
+            .components.set_index(self._parent.index)
+            .__finalize__(self._parent)
+        )
+
+    @property
+    def freq(self):
+        return self._get_values().inferred_freq
+
+
+@delegate_names(
+    delegate=PeriodArray, accessors=PeriodArray._datetimelike_ops, typ="property"
+)
+@delegate_names(
+    delegate=PeriodArray, accessors=PeriodArray._datetimelike_methods, typ="method"
+)
+class PeriodProperties(Properties):
+    """
+    Accessor object for datetimelike properties of the Series values.
+
+    Returns a Series indexed like the original Series.
+    Raises TypeError if the Series does not contain datetimelike values.
+
+    Examples
+    --------
+    >>> seconds_series = pd.Series(
+    ...     pd.period_range(
+    ...         start="2000-01-01 00:00:00", end="2000-01-01 00:00:03", freq="s"
+    ...     )
+    ... )
+    >>> seconds_series
+    0    2000-01-01 00:00:00
+    1    2000-01-01 00:00:01
+    2    2000-01-01 00:00:02
+    3    2000-01-01 00:00:03
+    dtype: period[s]
+    >>> seconds_series.dt.second
+    0    0
+    1    1
+    2    2
+    3    3
+    dtype: int64
+
+    >>> hours_series = pd.Series(
+    ...     pd.period_range(start="2000-01-01 00:00", end="2000-01-01 03:00", freq="h")
+    ... )
+    >>> hours_series
+    0    2000-01-01 00:00
+    1    2000-01-01 01:00
+    2    2000-01-01 02:00
+    3    2000-01-01 03:00
+    dtype: period[h]
+    >>> hours_series.dt.hour
+    0    0
+    1    1
+    2    2
+    3    3
+    dtype: int64
+
+    >>> quarters_series = pd.Series(
+    ...     pd.period_range(start="2000-01-01", end="2000-12-31", freq="Q-DEC")
+    ... )
+    >>> quarters_series
+    0    2000Q1
+    1    2000Q2
+    2    2000Q3
+    3    2000Q4
+    dtype: period[Q-DEC]
+    >>> quarters_series.dt.quarter
+    0    1
+    1    2
+    2    3
+    3    4
+    dtype: int64
+    """
+
+
+class CombinedDatetimelikeProperties(
+    DatetimeProperties, TimedeltaProperties, PeriodProperties
+):
+    def __new__(cls, data: Series):  # pyright: ignore[reportInconsistentConstructor]
+        # CombinedDatetimelikeProperties isn't really instantiated. Instead
+        # we need to choose which parent (datetime or timedelta) is
+        # appropriate. Since we're checking the dtypes anyway, we'll just
+        # do all the validation here.
+
+        if not isinstance(data, ABCSeries):
+            raise TypeError(
+                f"cannot convert an object of type {type(data)} to a datetimelike index"
+            )
+
+        orig = data if isinstance(data.dtype, CategoricalDtype) else None
+        if orig is not None:
+            data = data._constructor(
+                orig.array,
+                name=orig.name,
+                copy=False,
+                dtype=orig._values.categories.dtype,
+                index=orig.index,
+            )
+
+        if isinstance(data.dtype, ArrowDtype) and data.dtype.kind in "Mm":
+            return ArrowTemporalProperties(data, orig)
+        if lib.is_np_dtype(data.dtype, "M"):
+            return DatetimeProperties(data, orig)
+        elif isinstance(data.dtype, DatetimeTZDtype):
+            return DatetimeProperties(data, orig)
+        elif lib.is_np_dtype(data.dtype, "m"):
+            return TimedeltaProperties(data, orig)
+        elif isinstance(data.dtype, PeriodDtype):
+            return PeriodProperties(data, orig)
+
+        raise AttributeError("Can only use .dt accessor with datetimelike values")

shared/package-files/31/31d72adad79910126b22a5579ec9e179eee86674ecebe3fcff6f76916193af0e/blob

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+C 87 ; WX 600 ; N W ; B -3 -13 603 562 ;
+C 88 ; WX 600 ; N X ; B 23 0 577 562 ;
+C 89 ; WX 600 ; N Y ; B 24 0 576 562 ;
+C 90 ; WX 600 ; N Z ; B 86 0 514 562 ;
+C 91 ; WX 600 ; N bracketleft ; B 269 -108 442 622 ;
+C 92 ; WX 600 ; N backslash ; B 118 -80 482 629 ;
+C 93 ; WX 600 ; N bracketright ; B 158 -108 331 622 ;
+C 94 ; WX 600 ; N asciicircum ; B 94 354 506 622 ;
+C 95 ; WX 600 ; N underscore ; B 0 -125 600 -75 ;
+C 96 ; WX 600 ; N quoteleft ; B 224 328 387 562 ;
+C 97 ; WX 600 ; N a ; B 53 -15 559 441 ;
+C 98 ; WX 600 ; N b ; B 14 -15 575 629 ;
+C 99 ; WX 600 ; N c ; B 66 -15 529 441 ;
+C 100 ; WX 600 ; N d ; B 45 -15 591 629 ;
+C 101 ; WX 600 ; N e ; B 66 -15 548 441 ;
+C 102 ; WX 600 ; N f ; B 114 0 531 629 ; L i fi ; L l fl ;
+C 103 ; WX 600 ; N g ; B 45 -157 566 441 ;
+C 104 ; WX 600 ; N h ; B 18 0 582 629 ;
+C 105 ; WX 600 ; N i ; B 95 0 505 657 ;
+C 106 ; WX 600 ; N j ; B 82 -157 410 657 ;
+C 107 ; WX 600 ; N k ; B 43 0 580 629 ;
+C 108 ; WX 600 ; N l ; B 95 0 505 629 ;
+C 109 ; WX 600 ; N m ; B -5 0 605 441 ;
+C 110 ; WX 600 ; N n ; B 26 0 575 441 ;
+C 111 ; WX 600 ; N o ; B 62 -15 538 441 ;
+C 112 ; WX 600 ; N p ; B 9 -157 555 441 ;
+C 113 ; WX 600 ; N q ; B 45 -157 591 441 ;
+C 114 ; WX 600 ; N r ; B 60 0 559 441 ;
+C 115 ; WX 600 ; N s ; B 80 -15 513 441 ;
+C 116 ; WX 600 ; N t ; B 87 -15 530 561 ;
+C 117 ; WX 600 ; N u ; B 21 -15 562 426 ;
+C 118 ; WX 600 ; N v ; B 10 -10 590 426 ;
+C 119 ; WX 600 ; N w ; B -4 -10 604 426 ;
+C 120 ; WX 600 ; N x ; B 20 0 580 426 ;
+C 121 ; WX 600 ; N y ; B 7 -157 592 426 ;
+C 122 ; WX 600 ; N z ; B 99 0 502 426 ;
+C 123 ; WX 600 ; N braceleft ; B 182 -108 437 622 ;
+C 124 ; WX 600 ; N bar ; B 275 -250 326 750 ;
+C 125 ; WX 600 ; N braceright ; B 163 -108 418 622 ;
+C 126 ; WX 600 ; N asciitilde ; B 63 197 540 320 ;
+C 161 ; WX 600 ; N exclamdown ; B 236 -157 364 430 ;
+C 162 ; WX 600 ; N cent ; B 96 -49 500 614 ;
+C 163 ; WX 600 ; N sterling ; B 84 -21 521 611 ;
+C 164 ; WX 600 ; N fraction ; B 92 -57 509 665 ;
+C 165 ; WX 600 ; N yen ; B 26 0 574 562 ;
+C 166 ; WX 600 ; N florin ; B 4 -143 539 622 ;
+C 167 ; WX 600 ; N section ; B 113 -78 488 580 ;
+C 168 ; WX 600 ; N currency ; B 73 58 527 506 ;
+C 169 ; WX 600 ; N quotesingle ; B 259 328 341 562 ;
+C 170 ; WX 600 ; N quotedblleft ; B 143 328 471 562 ;
+C 171 ; WX 600 ; N guillemotleft ; B 37 70 563 446 ;
+C 172 ; WX 600 ; N guilsinglleft ; B 149 70 451 446 ;
+C 173 ; WX 600 ; N guilsinglright ; B 149 70 451 446 ;
+C 174 ; WX 600 ; N fi ; B 3 0 597 629 ;
+C 175 ; WX 600 ; N fl ; B 3 0 597 629 ;
+C 177 ; WX 600 ; N endash ; B 75 231 525 285 ;
+C 178 ; WX 600 ; N dagger ; B 141 -78 459 580 ;
+C 179 ; WX 600 ; N daggerdbl ; B 141 -78 459 580 ;
+C 180 ; WX 600 ; N periodcentered ; B 222 189 378 327 ;
+C 182 ; WX 600 ; N paragraph ; B 50 -78 511 562 ;
+C 183 ; WX 600 ; N bullet ; B 172 130 428 383 ;
+C 184 ; WX 600 ; N quotesinglbase ; B 213 -134 376 100 ;
+C 185 ; WX 600 ; N quotedblbase ; B 143 -134 457 100 ;
+C 186 ; WX 600 ; N quotedblright ; B 143 328 457 562 ;
+C 187 ; WX 600 ; N guillemotright ; B 37 70 563 446 ;
+C 188 ; WX 600 ; N ellipsis ; B 37 -15 563 111 ;
+C 189 ; WX 600 ; N perthousand ; B 3 -15 600 622 ;
+C 191 ; WX 600 ; N questiondown ; B 108 -157 471 430 ;
+C 193 ; WX 600 ; N grave ; B 151 497 378 672 ;
+C 194 ; WX 600 ; N acute ; B 242 497 469 672 ;
+C 195 ; WX 600 ; N circumflex ; B 124 477 476 654 ;
+C 196 ; WX 600 ; N tilde ; B 105 489 503 606 ;
+C 197 ; WX 600 ; N macron ; B 120 525 480 565 ;
+C 198 ; WX 600 ; N breve ; B 153 501 447 609 ;
+C 199 ; WX 600 ; N dotaccent ; B 249 537 352 640 ;
+C 200 ; WX 600 ; N dieresis ; B 148 537 453 640 ;
+C 202 ; WX 600 ; N ring ; B 218 463 382 627 ;
+C 203 ; WX 600 ; N cedilla ; B 224 -151 362 10 ;
+C 205 ; WX 600 ; N hungarumlaut ; B 133 497 540 672 ;
+C 206 ; WX 600 ; N ogonek ; B 211 -172 407 4 ;
+C 207 ; WX 600 ; N caron ; B 124 492 476 669 ;
+C 208 ; WX 600 ; N emdash ; B 0 231 600 285 ;
+C 225 ; WX 600 ; N AE ; B 3 0 550 562 ;
+C 227 ; WX 600 ; N ordfeminine ; B 156 249 442 580 ;
+C 232 ; WX 600 ; N Lslash ; B 47 0 554 562 ;
+C 233 ; WX 600 ; N Oslash ; B 43 -80 557 629 ;
+C 234 ; WX 600 ; N OE ; B 7 0 567 562 ;
+C 235 ; WX 600 ; N ordmasculine ; B 157 249 443 580 ;
+C 241 ; WX 600 ; N ae ; B 19 -15 570 441 ;
+C 245 ; WX 600 ; N dotlessi ; B 95 0 505 426 ;
+C 248 ; WX 600 ; N lslash ; B 95 0 505 629 ;
+C 249 ; WX 600 ; N oslash ; B 62 -80 538 506 ;
+C 250 ; WX 600 ; N oe ; B 19 -15 559 441 ;
+C 251 ; WX 600 ; N germandbls ; B 48 -15 588 629 ;
+C -1 ; WX 600 ; N Idieresis ; B 96 0 504 753 ;
+C -1 ; WX 600 ; N eacute ; B 66 -15 548 672 ;
+C -1 ; WX 600 ; N abreve ; B 53 -15 559 609 ;
+C -1 ; WX 600 ; N uhungarumlaut ; B 21 -15 580 672 ;
+C -1 ; WX 600 ; N ecaron ; B 66 -15 548 669 ;
+C -1 ; WX 600 ; N Ydieresis ; B 24 0 576 753 ;
+C -1 ; WX 600 ; N divide ; B 87 48 513 467 ;
+C -1 ; WX 600 ; N Yacute ; B 24 0 576 805 ;
+C -1 ; WX 600 ; N Acircumflex ; B 3 0 597 787 ;
+C -1 ; WX 600 ; N aacute ; B 53 -15 559 672 ;
+C -1 ; WX 600 ; N Ucircumflex ; B 17 -18 583 787 ;
+C -1 ; WX 600 ; N yacute ; B 7 -157 592 672 ;
+C -1 ; WX 600 ; N scommaaccent ; B 80 -250 513 441 ;
+C -1 ; WX 600 ; N ecircumflex ; B 66 -15 548 654 ;
+C -1 ; WX 600 ; N Uring ; B 17 -18 583 760 ;
+C -1 ; WX 600 ; N Udieresis ; B 17 -18 583 753 ;
+C -1 ; WX 600 ; N aogonek ; B 53 -172 587 441 ;
+C -1 ; WX 600 ; N Uacute ; B 17 -18 583 805 ;
+C -1 ; WX 600 ; N uogonek ; B 21 -172 590 426 ;
+C -1 ; WX 600 ; N Edieresis ; B 53 0 550 753 ;
+C -1 ; WX 600 ; N Dcroat ; B 30 0 574 562 ;
+C -1 ; WX 600 ; N commaaccent ; B 198 -250 335 -58 ;
+C -1 ; WX 600 ; N copyright ; B 0 -18 600 580 ;
+C -1 ; WX 600 ; N Emacron ; B 53 0 550 698 ;
+C -1 ; WX 600 ; N ccaron ; B 66 -15 529 669 ;
+C -1 ; WX 600 ; N aring ; B 53 -15 559 627 ;
+C -1 ; WX 600 ; N Ncommaaccent ; B 7 -250 593 562 ;
+C -1 ; WX 600 ; N lacute ; B 95 0 505 805 ;
+C -1 ; WX 600 ; N agrave ; B 53 -15 559 672 ;
+C -1 ; WX 600 ; N Tcommaaccent ; B 38 -250 563 562 ;
+C -1 ; WX 600 ; N Cacute ; B 41 -18 540 805 ;
+C -1 ; WX 600 ; N atilde ; B 53 -15 559 606 ;
+C -1 ; WX 600 ; N Edotaccent ; B 53 0 550 753 ;
+C -1 ; WX 600 ; N scaron ; B 80 -15 513 669 ;
+C -1 ; WX 600 ; N scedilla ; B 80 -151 513 441 ;
+C -1 ; WX 600 ; N iacute ; B 95 0 505 672 ;
+C -1 ; WX 600 ; N lozenge ; B 18 0 443 706 ;
+C -1 ; WX 600 ; N Rcaron ; B 38 0 588 802 ;
+C -1 ; WX 600 ; N Gcommaaccent ; B 31 -250 575 580 ;
+C -1 ; WX 600 ; N ucircumflex ; B 21 -15 562 654 ;
+C -1 ; WX 600 ; N acircumflex ; B 53 -15 559 654 ;
+C -1 ; WX 600 ; N Amacron ; B 3 0 597 698 ;
+C -1 ; WX 600 ; N rcaron ; B 60 0 559 669 ;
+C -1 ; WX 600 ; N ccedilla ; B 66 -151 529 441 ;
+C -1 ; WX 600 ; N Zdotaccent ; B 86 0 514 753 ;
+C -1 ; WX 600 ; N Thorn ; B 79 0 538 562 ;
+C -1 ; WX 600 ; N Omacron ; B 43 -18 557 698 ;
+C -1 ; WX 600 ; N Racute ; B 38 0 588 805 ;
+C -1 ; WX 600 ; N Sacute ; B 72 -20 529 805 ;
+C -1 ; WX 600 ; N dcaron ; B 45 -15 715 629 ;
+C -1 ; WX 600 ; N Umacron ; B 17 -18 583 698 ;
+C -1 ; WX 600 ; N uring ; B 21 -15 562 627 ;
+C -1 ; WX 600 ; N threesuperior ; B 155 240 406 622 ;
+C -1 ; WX 600 ; N Ograve ; B 43 -18 557 805 ;
+C -1 ; WX 600 ; N Agrave ; B 3 0 597 805 ;
+C -1 ; WX 600 ; N Abreve ; B 3 0 597 732 ;
+C -1 ; WX 600 ; N multiply ; B 87 43 515 470 ;
+C -1 ; WX 600 ; N uacute ; B 21 -15 562 672 ;
+C -1 ; WX 600 ; N Tcaron ; B 38 0 563 802 ;
+C -1 ; WX 600 ; N partialdiff ; B 17 -38 459 710 ;
+C -1 ; WX 600 ; N ydieresis ; B 7 -157 592 620 ;
+C -1 ; WX 600 ; N Nacute ; B 7 -13 593 805 ;
+C -1 ; WX 600 ; N icircumflex ; B 94 0 505 654 ;
+C -1 ; WX 600 ; N Ecircumflex ; B 53 0 550 787 ;
+C -1 ; WX 600 ; N adieresis ; B 53 -15 559 620 ;
+C -1 ; WX 600 ; N edieresis ; B 66 -15 548 620 ;
+C -1 ; WX 600 ; N cacute ; B 66 -15 529 672 ;
+C -1 ; WX 600 ; N nacute ; B 26 0 575 672 ;
+C -1 ; WX 600 ; N umacron ; B 21 -15 562 565 ;
+C -1 ; WX 600 ; N Ncaron ; B 7 -13 593 802 ;
+C -1 ; WX 600 ; N Iacute ; B 96 0 504 805 ;
+C -1 ; WX 600 ; N plusminus ; B 87 44 513 558 ;
+C -1 ; WX 600 ; N brokenbar ; B 275 -175 326 675 ;
+C -1 ; WX 600 ; N registered ; B 0 -18 600 580 ;
+C -1 ; WX 600 ; N Gbreve ; B 31 -18 575 732 ;
+C -1 ; WX 600 ; N Idotaccent ; B 96 0 504 753 ;
+C -1 ; WX 600 ; N summation ; B 15 -10 585 706 ;
+C -1 ; WX 600 ; N Egrave ; B 53 0 550 805 ;
+C -1 ; WX 600 ; N racute ; B 60 0 559 672 ;
+C -1 ; WX 600 ; N omacron ; B 62 -15 538 565 ;
+C -1 ; WX 600 ; N Zacute ; B 86 0 514 805 ;
+C -1 ; WX 600 ; N Zcaron ; B 86 0 514 802 ;
+C -1 ; WX 600 ; N greaterequal ; B 98 0 502 710 ;
+C -1 ; WX 600 ; N Eth ; B 30 0 574 562 ;
+C -1 ; WX 600 ; N Ccedilla ; B 41 -151 540 580 ;
+C -1 ; WX 600 ; N lcommaaccent ; B 95 -250 505 629 ;
+C -1 ; WX 600 ; N tcaron ; B 87 -15 530 717 ;
+C -1 ; WX 600 ; N eogonek ; B 66 -172 548 441 ;
+C -1 ; WX 600 ; N Uogonek ; B 17 -172 583 562 ;
+C -1 ; WX 600 ; N Aacute ; B 3 0 597 805 ;
+C -1 ; WX 600 ; N Adieresis ; B 3 0 597 753 ;
+C -1 ; WX 600 ; N egrave ; B 66 -15 548 672 ;
+C -1 ; WX 600 ; N zacute ; B 99 0 502 672 ;
+C -1 ; WX 600 ; N iogonek ; B 95 -172 505 657 ;
+C -1 ; WX 600 ; N Oacute ; B 43 -18 557 805 ;
+C -1 ; WX 600 ; N oacute ; B 62 -15 538 672 ;
+C -1 ; WX 600 ; N amacron ; B 53 -15 559 565 ;
+C -1 ; WX 600 ; N sacute ; B 80 -15 513 672 ;
+C -1 ; WX 600 ; N idieresis ; B 95 0 505 620 ;
+C -1 ; WX 600 ; N Ocircumflex ; B 43 -18 557 787 ;
+C -1 ; WX 600 ; N Ugrave ; B 17 -18 583 805 ;
+C -1 ; WX 600 ; N Delta ; B 6 0 598 688 ;
+C -1 ; WX 600 ; N thorn ; B -6 -157 555 629 ;
+C -1 ; WX 600 ; N twosuperior ; B 177 249 424 622 ;
+C -1 ; WX 600 ; N Odieresis ; B 43 -18 557 753 ;
+C -1 ; WX 600 ; N mu ; B 21 -157 562 426 ;
+C -1 ; WX 600 ; N igrave ; B 95 0 505 672 ;
+C -1 ; WX 600 ; N ohungarumlaut ; B 62 -15 580 672 ;
+C -1 ; WX 600 ; N Eogonek ; B 53 -172 561 562 ;
+C -1 ; WX 600 ; N dcroat ; B 45 -15 591 629 ;
+C -1 ; WX 600 ; N threequarters ; B 8 -56 593 666 ;
+C -1 ; WX 600 ; N Scedilla ; B 72 -151 529 580 ;
+C -1 ; WX 600 ; N lcaron ; B 95 0 533 629 ;
+C -1 ; WX 600 ; N Kcommaaccent ; B 38 -250 582 562 ;
+C -1 ; WX 600 ; N Lacute ; B 47 0 554 805 ;
+C -1 ; WX 600 ; N trademark ; B -23 263 623 562 ;
+C -1 ; WX 600 ; N edotaccent ; B 66 -15 548 620 ;
+C -1 ; WX 600 ; N Igrave ; B 96 0 504 805 ;
+C -1 ; WX 600 ; N Imacron ; B 96 0 504 698 ;
+C -1 ; WX 600 ; N Lcaron ; B 47 0 554 562 ;
+C -1 ; WX 600 ; N onehalf ; B 0 -57 611 665 ;
+C -1 ; WX 600 ; N lessequal ; B 98 0 502 710 ;
+C -1 ; WX 600 ; N ocircumflex ; B 62 -15 538 654 ;
+C -1 ; WX 600 ; N ntilde ; B 26 0 575 606 ;
+C -1 ; WX 600 ; N Uhungarumlaut ; B 17 -18 590 805 ;
+C -1 ; WX 600 ; N Eacute ; B 53 0 550 805 ;
+C -1 ; WX 600 ; N emacron ; B 66 -15 548 565 ;
+C -1 ; WX 600 ; N gbreve ; B 45 -157 566 609 ;
+C -1 ; WX 600 ; N onequarter ; B 0 -57 600 665 ;
+C -1 ; WX 600 ; N Scaron ; B 72 -20 529 802 ;
+C -1 ; WX 600 ; N Scommaaccent ; B 72 -250 529 580 ;
+C -1 ; WX 600 ; N Ohungarumlaut ; B 43 -18 580 805 ;
+C -1 ; WX 600 ; N degree ; B 123 269 477 622 ;
+C -1 ; WX 600 ; N ograve ; B 62 -15 538 672 ;
+C -1 ; WX 600 ; N Ccaron ; B 41 -18 540 802 ;
+C -1 ; WX 600 ; N ugrave ; B 21 -15 562 672 ;
+C -1 ; WX 600 ; N radical ; B 3 -15 597 792 ;
+C -1 ; WX 600 ; N Dcaron ; B 43 0 574 802 ;
+C -1 ; WX 600 ; N rcommaaccent ; B 60 -250 559 441 ;
+C -1 ; WX 600 ; N Ntilde ; B 7 -13 593 729 ;
+C -1 ; WX 600 ; N otilde ; B 62 -15 538 606 ;
+C -1 ; WX 600 ; N Rcommaaccent ; B 38 -250 588 562 ;
+C -1 ; WX 600 ; N Lcommaaccent ; B 47 -250 554 562 ;
+C -1 ; WX 600 ; N Atilde ; B 3 0 597 729 ;
+C -1 ; WX 600 ; N Aogonek ; B 3 -172 608 562 ;
+C -1 ; WX 600 ; N Aring ; B 3 0 597 750 ;
+C -1 ; WX 600 ; N Otilde ; B 43 -18 557 729 ;
+C -1 ; WX 600 ; N zdotaccent ; B 99 0 502 620 ;
+C -1 ; WX 600 ; N Ecaron ; B 53 0 550 802 ;
+C -1 ; WX 600 ; N Iogonek ; B 96 -172 504 562 ;
+C -1 ; WX 600 ; N kcommaaccent ; B 43 -250 580 629 ;
+C -1 ; WX 600 ; N minus ; B 80 232 520 283 ;
+C -1 ; WX 600 ; N Icircumflex ; B 96 0 504 787 ;
+C -1 ; WX 600 ; N ncaron ; B 26 0 575 669 ;
+C -1 ; WX 600 ; N tcommaaccent ; B 87 -250 530 561 ;
+C -1 ; WX 600 ; N logicalnot ; B 87 108 513 369 ;
+C -1 ; WX 600 ; N odieresis ; B 62 -15 538 620 ;
+C -1 ; WX 600 ; N udieresis ; B 21 -15 562 620 ;
+C -1 ; WX 600 ; N notequal ; B 15 -16 540 529 ;
+C -1 ; WX 600 ; N gcommaaccent ; B 45 -157 566 708 ;
+C -1 ; WX 600 ; N eth ; B 62 -15 538 629 ;
+C -1 ; WX 600 ; N zcaron ; B 99 0 502 669 ;
+C -1 ; WX 600 ; N ncommaaccent ; B 26 -250 575 441 ;
+C -1 ; WX 600 ; N onesuperior ; B 172 249 428 622 ;
+C -1 ; WX 600 ; N imacron ; B 95 0 505 565 ;
+C -1 ; WX 600 ; N Euro ; B 0 0 0 0 ;
+EndCharMetrics
+EndFontMetrics

shared/package-files/31/31dda61e64849ebf334f5965cf81ea18bf3e7ac057f316cf84a3a50022d503d2/blob

@@ -0,0 +1,1575 @@
+import abc
+import cmath
+import collections.abc
+import contextlib
+import warnings
+from typing import (
+    Any,
+    Callable,
+    Collection,
+    Dict,
+    List,
+    NoReturn,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
+)
+
+import torch
+
+try:
+    import numpy as np
+
+    NUMPY_AVAILABLE = True
+except ModuleNotFoundError:
+    NUMPY_AVAILABLE = False
+
+
+class ErrorMeta(Exception):
+    """Internal testing exception that makes that carries error metadata."""
+
+    def __init__(
+        self, type: Type[Exception], msg: str, *, id: Tuple[Any, ...] = ()
+    ) -> None:
+        super().__init__(
+            "If you are a user and see this message during normal operation "
+            "please file an issue at https://github.com/pytorch/pytorch/issues. "
+            "If you are a developer and working on the comparison functions, please `raise ErrorMeta().to_error()` "
+            "for user facing errors."
+        )
+        self.type = type
+        self.msg = msg
+        self.id = id
+
+    def to_error(
+        self, msg: Optional[Union[str, Callable[[str], str]]] = None
+    ) -> Exception:
+        if not isinstance(msg, str):
+            generated_msg = self.msg
+            if self.id:
+                generated_msg += f"\n\nThe failure occurred for item {''.join(str([item]) for item in self.id)}"
+
+            msg = msg(generated_msg) if callable(msg) else generated_msg
+
+        return self.type(msg)
+
+
+# Some analysis of tolerance by logging tests from test_torch.py can be found in
+# https://github.com/pytorch/pytorch/pull/32538.
+# {dtype: (rtol, atol)}
+_DTYPE_PRECISIONS = {
+    torch.float16: (0.001, 1e-5),
+    torch.bfloat16: (0.016, 1e-5),
+    torch.float32: (1.3e-6, 1e-5),
+    torch.float64: (1e-7, 1e-7),
+    torch.complex32: (0.001, 1e-5),
+    torch.complex64: (1.3e-6, 1e-5),
+    torch.complex128: (1e-7, 1e-7),
+}
+# The default tolerances of torch.float32 are used for quantized dtypes, because quantized tensors are compared in
+# their dequantized and floating point representation. For more details see `TensorLikePair._compare_quantized_values`
+_DTYPE_PRECISIONS.update(
+    dict.fromkeys(
+        (torch.quint8, torch.quint2x4, torch.quint4x2, torch.qint8, torch.qint32),
+        _DTYPE_PRECISIONS[torch.float32],
+    )
+)
+
+
+def default_tolerances(
+    *inputs: Union[torch.Tensor, torch.dtype],
+    dtype_precisions: Optional[Dict[torch.dtype, Tuple[float, float]]] = None,
+) -> Tuple[float, float]:
+    """Returns the default absolute and relative testing tolerances for a set of inputs based on the dtype.
+
+    See :func:`assert_close` for a table of the default tolerance for each dtype.
+
+    Returns:
+        (Tuple[float, float]): Loosest tolerances of all input dtypes.
+    """
+    dtypes = []
+    for input in inputs:
+        if isinstance(input, torch.Tensor):
+            dtypes.append(input.dtype)
+        elif isinstance(input, torch.dtype):
+            dtypes.append(input)
+        else:
+            raise TypeError(
+                f"Expected a torch.Tensor or a torch.dtype, but got {type(input)} instead."
+            )
+    dtype_precisions = dtype_precisions or _DTYPE_PRECISIONS
+    rtols, atols = zip(*[dtype_precisions.get(dtype, (0.0, 0.0)) for dtype in dtypes])
+    return max(rtols), max(atols)
+
+
+def get_tolerances(
+    *inputs: Union[torch.Tensor, torch.dtype],
+    rtol: Optional[float],
+    atol: Optional[float],
+    id: Tuple[Any, ...] = (),
+) -> Tuple[float, float]:
+    """Gets absolute and relative to be used for numeric comparisons.
+
+    If both ``rtol`` and ``atol`` are specified, this is a no-op. If both are not specified, the return value of
+    :func:`default_tolerances` is used.
+
+    Raises:
+        ErrorMeta: With :class:`ValueError`, if only ``rtol`` or ``atol`` is specified.
+
+    Returns:
+        (Tuple[float, float]): Valid absolute and relative tolerances.
+    """
+    if (rtol is None) ^ (atol is None):
+        # We require both tolerance to be omitted or specified, because specifying only one might lead to surprising
+        # results. Imagine setting atol=0.0 and the tensors still match because rtol>0.0.
+        raise ErrorMeta(
+            ValueError,
+            f"Both 'rtol' and 'atol' must be either specified or omitted, "
+            f"but got no {'rtol' if rtol is None else 'atol'}.",
+            id=id,
+        )
+    elif rtol is not None and atol is not None:
+        return rtol, atol
+    else:
+        return default_tolerances(*inputs)
+
+
+def _make_mismatch_msg(
+    *,
+    default_identifier: str,
+    identifier: Optional[Union[str, Callable[[str], str]]] = None,
+    extra: Optional[str] = None,
+    abs_diff: float,
+    abs_diff_idx: Optional[Union[int, Tuple[int, ...]]] = None,
+    atol: float,
+    rel_diff: float,
+    rel_diff_idx: Optional[Union[int, Tuple[int, ...]]] = None,
+    rtol: float,
+) -> str:
+    """Makes a mismatch error message for numeric values.
+
+    Args:
+        default_identifier (str): Default description of the compared values, e.g. "Tensor-likes".
+        identifier (Optional[Union[str, Callable[[str], str]]]): Optional identifier that overrides
+            ``default_identifier``. Can be passed as callable in which case it will be called with
+            ``default_identifier`` to create the description at runtime.
+        extra (Optional[str]): Extra information to be placed after the message header and the mismatch statistics.
+        abs_diff (float): Absolute difference.
+        abs_diff_idx (Optional[Union[int, Tuple[int, ...]]]): Optional index of the absolute difference.
+        atol (float): Allowed absolute tolerance. Will only be added to mismatch statistics if it or ``rtol`` are
+            ``> 0``.
+        rel_diff (float): Relative difference.
+        rel_diff_idx (Optional[Union[int, Tuple[int, ...]]]): Optional index of the relative difference.
+        rtol (float): Allowed relative tolerance. Will only be added to mismatch statistics if it or ``atol`` are
+            ``> 0``.
+    """
+    equality = rtol == 0 and atol == 0
+
+    def make_diff_msg(
+        *,
+        type: str,
+        diff: float,
+        idx: Optional[Union[int, Tuple[int, ...]]],
+        tol: float,
+    ) -> str:
+        if idx is None:
+            msg = f"{type.title()} difference: {diff}"
+        else:
+            msg = f"Greatest {type} difference: {diff} at index {idx}"
+        if not equality:
+            msg += f" (up to {tol} allowed)"
+        return msg + "\n"
+
+    if identifier is None:
+        identifier = default_identifier
+    elif callable(identifier):
+        identifier = identifier(default_identifier)
+
+    msg = f"{identifier} are not {'equal' if equality else 'close'}!\n\n"
+
+    if extra:
+        msg += f"{extra.strip()}\n"
+
+    msg += make_diff_msg(type="absolute", diff=abs_diff, idx=abs_diff_idx, tol=atol)
+    msg += make_diff_msg(type="relative", diff=rel_diff, idx=rel_diff_idx, tol=rtol)
+
+    return msg.strip()
+
+
+def make_scalar_mismatch_msg(
+    actual: Union[bool, int, float, complex],
+    expected: Union[bool, int, float, complex],
+    *,
+    rtol: float,
+    atol: float,
+    identifier: Optional[Union[str, Callable[[str], str]]] = None,
+) -> str:
+    """Makes a mismatch error message for scalars.
+
+    Args:
+        actual (Union[bool, int, float, complex]): Actual scalar.
+        expected (Union[bool, int, float, complex]): Expected scalar.
+        rtol (float): Relative tolerance.
+        atol (float): Absolute tolerance.
+        identifier (Optional[Union[str, Callable[[str], str]]]): Optional description for the scalars. Can be passed
+            as callable in which case it will be called by the default value to create the description at runtime.
+            Defaults to "Scalars".
+    """
+    abs_diff = abs(actual - expected)
+    rel_diff = float("inf") if expected == 0 else abs_diff / abs(expected)
+    return _make_mismatch_msg(
+        default_identifier="Scalars",
+        identifier=identifier,
+        extra=f"Expected {expected} but got {actual}.",
+        abs_diff=abs_diff,
+        atol=atol,
+        rel_diff=rel_diff,
+        rtol=rtol,
+    )
+
+
+def make_tensor_mismatch_msg(
+    actual: torch.Tensor,
+    expected: torch.Tensor,
+    matches: torch.Tensor,
+    *,
+    rtol: float,
+    atol: float,
+    identifier: Optional[Union[str, Callable[[str], str]]] = None,
+):
+    """Makes a mismatch error message for tensors.
+
+    Args:
+        actual (torch.Tensor): Actual tensor.
+        expected (torch.Tensor): Expected tensor.
+        matches (torch.Tensor): Boolean mask of the same shape as ``actual`` and ``expected`` that indicates the
+            location of matches.
+        rtol (float): Relative tolerance.
+        atol (float): Absolute tolerance.
+        identifier (Optional[Union[str, Callable[[str], str]]]): Optional description for the tensors. Can be passed
+            as callable in which case it will be called by the default value to create the description at runtime.
+            Defaults to "Tensor-likes".
+    """
+
+    def unravel_flat_index(flat_index: int) -> Tuple[int, ...]:
+        if not matches.shape:
+            return ()
+
+        inverse_index = []
+        for size in matches.shape[::-1]:
+            div, mod = divmod(flat_index, size)
+            flat_index = div
+            inverse_index.append(mod)
+
+        return tuple(inverse_index[::-1])
+
+    number_of_elements = matches.numel()
+    total_mismatches = number_of_elements - int(torch.sum(matches))
+    extra = (
+        f"Mismatched elements: {total_mismatches} / {number_of_elements} "
+        f"({total_mismatches / number_of_elements:.1%})"
+    )
+
+    actual_flat = actual.flatten()
+    expected_flat = expected.flatten()
+    matches_flat = matches.flatten()
+
+    if not actual.dtype.is_floating_point and not actual.dtype.is_complex:
+        # TODO: Instead of always upcasting to int64, it would be sufficient to cast to the next higher dtype to avoid
+        #  overflow
+        actual_flat = actual_flat.to(torch.int64)
+        expected_flat = expected_flat.to(torch.int64)
+
+    abs_diff = torch.abs(actual_flat - expected_flat)
+    # Ensure that only mismatches are used for the max_abs_diff computation
+    abs_diff[matches_flat] = 0
+    max_abs_diff, max_abs_diff_flat_idx = torch.max(abs_diff, 0)
+
+    rel_diff = abs_diff / torch.abs(expected_flat)
+    # Ensure that only mismatches are used for the max_rel_diff computation
+    rel_diff[matches_flat] = 0
+    max_rel_diff, max_rel_diff_flat_idx = torch.max(rel_diff, 0)
+    return _make_mismatch_msg(
+        default_identifier="Tensor-likes",
+        identifier=identifier,
+        extra=extra,
+        abs_diff=max_abs_diff.item(),
+        abs_diff_idx=unravel_flat_index(int(max_abs_diff_flat_idx)),
+        atol=atol,
+        rel_diff=max_rel_diff.item(),
+        rel_diff_idx=unravel_flat_index(int(max_rel_diff_flat_idx)),
+        rtol=rtol,
+    )
+
+
+class UnsupportedInputs(Exception):  # noqa: B903
+    """Exception to be raised during the construction of a :class:`Pair` in case it doesn't support the inputs."""
+
+
+class Pair(abc.ABC):
+    """ABC for all comparison pairs to be used in conjunction with :func:`assert_equal`.
+
+    Each subclass needs to overwrite :meth:`Pair.compare` that performs the actual comparison.
+
+    Each pair receives **all** options, so select the ones applicable for the subclass and forward the rest to the
+    super class. Raising an :class:`UnsupportedInputs` during constructions indicates that the pair is not able to
+    handle the inputs and the next pair type will be tried.
+
+    All other errors should be raised as :class:`ErrorMeta`. After the instantiation, :meth:`Pair._make_error_meta` can
+    be used to automatically handle overwriting the message with a user supplied one and id handling.
+    """
+
+    def __init__(
+        self,
+        actual: Any,
+        expected: Any,
+        *,
+        id: Tuple[Any, ...] = (),
+        **unknown_parameters: Any,
+    ) -> None:
+        self.actual = actual
+        self.expected = expected
+        self.id = id
+        self._unknown_parameters = unknown_parameters
+
+    @staticmethod
+    def _inputs_not_supported() -> NoReturn:
+        raise UnsupportedInputs()
+
+    @staticmethod
+    def _check_inputs_isinstance(*inputs: Any, cls: Union[Type, Tuple[Type, ...]]):
+        """Checks if all inputs are instances of a given class and raise :class:`UnsupportedInputs` otherwise."""
+        if not all(isinstance(input, cls) for input in inputs):
+            Pair._inputs_not_supported()
+
+    def _fail(
+        self, type: Type[Exception], msg: str, *, id: Tuple[Any, ...] = ()
+    ) -> NoReturn:
+        """Raises an :class:`ErrorMeta` from a given exception type and message and the stored id.
+
+        .. warning::
+
+            If you use this before the ``super().__init__(...)`` call in the constructor, you have to pass the ``id``
+            explicitly.
+        """
+        raise ErrorMeta(type, msg, id=self.id if not id and hasattr(self, "id") else id)
+
+    @abc.abstractmethod
+    def compare(self) -> None:
+        """Compares the inputs and raises an :class`ErrorMeta` in case they mismatch."""
+
+    def extra_repr(self) -> Sequence[Union[str, Tuple[str, Any]]]:
+        """Returns extra information that will be included in the representation.
+
+        Should be overwritten by all subclasses that use additional options. The representation of the object will only
+        be surfaced in case we encounter an unexpected error and thus should help debug the issue. Can be a sequence of
+        key-value-pairs or attribute names.
+        """
+        return []
+
+    def __repr__(self) -> str:
+        head = f"{type(self).__name__}("
+        tail = ")"
+        body = [
+            f"    {name}={value!s},"
+            for name, value in [
+                ("id", self.id),
+                ("actual", self.actual),
+                ("expected", self.expected),
+                *[
+                    (extra, getattr(self, extra)) if isinstance(extra, str) else extra
+                    for extra in self.extra_repr()
+                ],
+            ]
+        ]
+        return "\n".join((head, *body, *tail))
+
+
+class ObjectPair(Pair):
+    """Pair for any type of inputs that will be compared with the `==` operator.
+
+    .. note::
+
+        Since this will instantiate for any kind of inputs, it should only be used as fallback after all other pairs
+        couldn't handle the inputs.
+
+    """
+
+    def compare(self) -> None:
+        try:
+            equal = self.actual == self.expected
+        except Exception as error:
+            # We are not using `self._raise_error_meta` here since we need the exception chaining
+            raise ErrorMeta(
+                ValueError,
+                f"{self.actual} == {self.expected} failed with:\n{error}.",
+                id=self.id,
+            ) from error
+
+        if not equal:
+            self._fail(AssertionError, f"{self.actual} != {self.expected}")
+
+
+class NonePair(Pair):
+    """Pair for ``None`` inputs."""
+
+    def __init__(self, actual: Any, expected: Any, **other_parameters: Any) -> None:
+        if not (actual is None or expected is None):
+            self._inputs_not_supported()
+
+        super().__init__(actual, expected, **other_parameters)
+
+    def compare(self) -> None:
+        if not (self.actual is None and self.expected is None):
+            self._fail(
+                AssertionError, f"None mismatch: {self.actual} is not {self.expected}"
+            )
+
+
+class BooleanPair(Pair):
+    """Pair for :class:`bool` inputs.
+
+    .. note::
+
+        If ``numpy`` is available, also handles :class:`numpy.bool_` inputs.
+
+    """
+
+    def __init__(
+        self,
+        actual: Any,
+        expected: Any,
+        *,
+        id: Tuple[Any, ...],
+        **other_parameters: Any,
+    ) -> None:
+        actual, expected = self._process_inputs(actual, expected, id=id)
+        super().__init__(actual, expected, **other_parameters)
+
+    @property
+    def _supported_types(self) -> Tuple[Type, ...]:
+        cls: List[Type] = [bool]
+        if NUMPY_AVAILABLE:
+            cls.append(np.bool_)
+        return tuple(cls)
+
+    def _process_inputs(
+        self, actual: Any, expected: Any, *, id: Tuple[Any, ...]
+    ) -> Tuple[bool, bool]:
+        self._check_inputs_isinstance(actual, expected, cls=self._supported_types)
+        actual, expected = (
+            self._to_bool(bool_like, id=id) for bool_like in (actual, expected)
+        )
+        return actual, expected
+
+    def _to_bool(self, bool_like: Any, *, id: Tuple[Any, ...]) -> bool:
+        if isinstance(bool_like, bool):
+            return bool_like
+        elif isinstance(bool_like, np.bool_):
+            return bool_like.item()
+        else:
+            raise ErrorMeta(
+                TypeError, f"Unknown boolean type {type(bool_like)}.", id=id
+            )
+
+    def compare(self) -> None:
+        if self.actual is not self.expected:
+            self._fail(
+                AssertionError,
+                f"Booleans mismatch: {self.actual} is not {self.expected}",
+            )
+
+
+class NumberPair(Pair):
+    """Pair for Python number (:class:`int`, :class:`float`, and :class:`complex`) inputs.
+
+    .. note::
+
+        If ``numpy`` is available, also handles :class:`numpy.number` inputs.
+
+    Kwargs:
+        rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
+            values based on the type are selected with the below table.
+        atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
+            values based on the type are selected with the below table.
+        equal_nan (bool): If ``True``, two ``NaN`` values are considered equal. Defaults to ``False``.
+        check_dtype (bool): If ``True``, the type of the inputs will be checked for equality. Defaults to ``False``.
+
+    The following table displays correspondence between Python number type and the ``torch.dtype``'s. See
+    :func:`assert_close` for the corresponding tolerances.
+
+    +------------------+-------------------------------+
+    | ``type``         | corresponding ``torch.dtype`` |
+    +==================+===============================+
+    | :class:`int`     | :attr:`~torch.int64`          |
+    +------------------+-------------------------------+
+    | :class:`float`   | :attr:`~torch.float64`        |
+    +------------------+-------------------------------+
+    | :class:`complex` | :attr:`~torch.complex64`      |
+    +------------------+-------------------------------+
+    """
+
+    _TYPE_TO_DTYPE = {
+        int: torch.int64,
+        float: torch.float64,
+        complex: torch.complex128,
+    }
+    _NUMBER_TYPES = tuple(_TYPE_TO_DTYPE.keys())
+
+    def __init__(
+        self,
+        actual: Any,
+        expected: Any,
+        *,
+        id: Tuple[Any, ...] = (),
+        rtol: Optional[float] = None,
+        atol: Optional[float] = None,
+        equal_nan: bool = False,
+        check_dtype: bool = False,
+        **other_parameters: Any,
+    ) -> None:
+        actual, expected = self._process_inputs(actual, expected, id=id)
+        super().__init__(actual, expected, id=id, **other_parameters)
+
+        self.rtol, self.atol = get_tolerances(
+            *[self._TYPE_TO_DTYPE[type(input)] for input in (actual, expected)],
+            rtol=rtol,
+            atol=atol,
+            id=id,
+        )
+        self.equal_nan = equal_nan
+        self.check_dtype = check_dtype
+
+    @property
+    def _supported_types(self) -> Tuple[Type, ...]:
+        cls = list(self._NUMBER_TYPES)
+        if NUMPY_AVAILABLE:
+            cls.append(np.number)
+        return tuple(cls)
+
+    def _process_inputs(
+        self, actual: Any, expected: Any, *, id: Tuple[Any, ...]
+    ) -> Tuple[Union[int, float, complex], Union[int, float, complex]]:
+        self._check_inputs_isinstance(actual, expected, cls=self._supported_types)
+        actual, expected = (
+            self._to_number(number_like, id=id) for number_like in (actual, expected)
+        )
+        return actual, expected
+
+    def _to_number(
+        self, number_like: Any, *, id: Tuple[Any, ...]
+    ) -> Union[int, float, complex]:
+        if NUMPY_AVAILABLE and isinstance(number_like, np.number):
+            return number_like.item()
+        elif isinstance(number_like, self._NUMBER_TYPES):
+            return number_like  # type: ignore[return-value]
+        else:
+            raise ErrorMeta(
+                TypeError, f"Unknown number type {type(number_like)}.", id=id
+            )
+
+    def compare(self) -> None:
+        if self.check_dtype and type(self.actual) is not type(self.expected):
+            self._fail(
+                AssertionError,
+                f"The (d)types do not match: {type(self.actual)} != {type(self.expected)}.",
+            )
+
+        if self.actual == self.expected:
+            return
+
+        if self.equal_nan and cmath.isnan(self.actual) and cmath.isnan(self.expected):
+            return
+
+        abs_diff = abs(self.actual - self.expected)
+        tolerance = self.atol + self.rtol * abs(self.expected)
+
+        if cmath.isfinite(abs_diff) and abs_diff <= tolerance:
+            return
+
+        self._fail(
+            AssertionError,
+            make_scalar_mismatch_msg(
+                self.actual, self.expected, rtol=self.rtol, atol=self.atol
+            ),
+        )
+
+    def extra_repr(self) -> Sequence[str]:
+        return (
+            "rtol",
+            "atol",
+            "equal_nan",
+            "check_dtype",
+        )
+
+
+class TensorLikePair(Pair):
+    """Pair for :class:`torch.Tensor`-like inputs.
+
+    Kwargs:
+        allow_subclasses (bool):
+        rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
+            values based on the type are selected. See :func:assert_close: for details.
+        atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
+            values based on the type are selected. See :func:assert_close: for details.
+        equal_nan (bool): If ``True``, two ``NaN`` values are considered equal. Defaults to ``False``.
+        check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
+            :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
+            :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
+        check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
+            check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
+            :func:`torch.promote_types`) before being compared.
+        check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
+            check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
+            compared.
+        check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
+    """
+
+    def __init__(
+        self,
+        actual: Any,
+        expected: Any,
+        *,
+        id: Tuple[Any, ...] = (),
+        allow_subclasses: bool = True,
+        rtol: Optional[float] = None,
+        atol: Optional[float] = None,
+        equal_nan: bool = False,
+        check_device: bool = True,
+        check_dtype: bool = True,
+        check_layout: bool = True,
+        check_stride: bool = False,
+        **other_parameters: Any,
+    ):
+        actual, expected = self._process_inputs(
+            actual, expected, id=id, allow_subclasses=allow_subclasses
+        )
+        super().__init__(actual, expected, id=id, **other_parameters)
+
+        self.rtol, self.atol = get_tolerances(
+            actual, expected, rtol=rtol, atol=atol, id=self.id
+        )
+        self.equal_nan = equal_nan
+        self.check_device = check_device
+        self.check_dtype = check_dtype
+        self.check_layout = check_layout
+        self.check_stride = check_stride
+
+    def _process_inputs(
+        self, actual: Any, expected: Any, *, id: Tuple[Any, ...], allow_subclasses: bool
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        directly_related = isinstance(actual, type(expected)) or isinstance(
+            expected, type(actual)
+        )
+        if not directly_related:
+            self._inputs_not_supported()
+
+        if not allow_subclasses and type(actual) is not type(expected):
+            self._inputs_not_supported()
+
+        actual, expected = (self._to_tensor(input) for input in (actual, expected))
+        for tensor in (actual, expected):
+            self._check_supported(tensor, id=id)
+        return actual, expected
+
+    def _to_tensor(self, tensor_like: Any) -> torch.Tensor:
+        if isinstance(tensor_like, torch.Tensor):
+            return tensor_like
+
+        try:
+            return torch.as_tensor(tensor_like)
+        except Exception:
+            self._inputs_not_supported()
+
+    def _check_supported(self, tensor: torch.Tensor, *, id: Tuple[Any, ...]) -> None:
+        if tensor.layout not in {
+            torch.strided,
+            torch.sparse_coo,
+            torch.sparse_csr,
+            torch.sparse_csc,
+            torch.sparse_bsr,
+            torch.sparse_bsc,
+        }:
+            raise ErrorMeta(
+                ValueError, f"Unsupported tensor layout {tensor.layout}", id=id
+            )
+
+    def compare(self) -> None:
+        actual, expected = self.actual, self.expected
+
+        self._compare_attributes(actual, expected)
+        if any(input.device.type == "meta" for input in (actual, expected)):
+            return
+
+        actual, expected = self._equalize_attributes(actual, expected)
+        self._compare_values(actual, expected)
+
+    def _compare_attributes(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+    ) -> None:
+        """Checks if the attributes of two tensors match.
+
+        Always checks
+
+        - the :attr:`~torch.Tensor.shape`,
+        - whether both inputs are quantized or not,
+        - and if they use the same quantization scheme.
+
+        Checks for
+
+        - :attr:`~torch.Tensor.layout`,
+        - :meth:`~torch.Tensor.stride`,
+        - :attr:`~torch.Tensor.device`, and
+        - :attr:`~torch.Tensor.dtype`
+
+        are optional and can be disabled through the corresponding ``check_*`` flag during construction of the pair.
+        """
+
+        def raise_mismatch_error(
+            attribute_name: str, actual_value: Any, expected_value: Any
+        ) -> NoReturn:
+            self._fail(
+                AssertionError,
+                f"The values for attribute '{attribute_name}' do not match: {actual_value} != {expected_value}.",
+            )
+
+        if actual.shape != expected.shape:
+            raise_mismatch_error("shape", actual.shape, expected.shape)
+
+        if actual.is_quantized != expected.is_quantized:
+            raise_mismatch_error(
+                "is_quantized", actual.is_quantized, expected.is_quantized
+            )
+        elif actual.is_quantized and actual.qscheme() != expected.qscheme():
+            raise_mismatch_error("qscheme()", actual.qscheme(), expected.qscheme())
+
+        if actual.layout != expected.layout:
+            if self.check_layout:
+                raise_mismatch_error("layout", actual.layout, expected.layout)
+        elif (
+            actual.layout == torch.strided
+            and self.check_stride
+            and actual.stride() != expected.stride()
+        ):
+            raise_mismatch_error("stride()", actual.stride(), expected.stride())
+
+        if self.check_device and actual.device != expected.device:
+            raise_mismatch_error("device", actual.device, expected.device)
+
+        if self.check_dtype and actual.dtype != expected.dtype:
+            raise_mismatch_error("dtype", actual.dtype, expected.dtype)
+
+    def _equalize_attributes(
+        self, actual: torch.Tensor, expected: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Equalizes some attributes of two tensors for value comparison.
+
+        If ``actual`` and ``expected`` are ...
+
+        - ... not on the same :attr:`~torch.Tensor.device`, they are moved CPU memory.
+        - ... not of the same ``dtype``, they are promoted  to a common ``dtype`` (according to
+            :func:`torch.promote_types`).
+        - ... not of the same ``layout``, they are converted to strided tensors.
+
+        Args:
+            actual (Tensor): Actual tensor.
+            expected (Tensor): Expected tensor.
+
+        Returns:
+            (Tuple[Tensor, Tensor]): Equalized tensors.
+        """
+        # The comparison logic uses operators currently not supported by the MPS backends.
+        #  See https://github.com/pytorch/pytorch/issues/77144 for details.
+        # TODO: Remove this conversion as soon as all operations are supported natively by the MPS backend
+        if actual.is_mps or expected.is_mps:  # type: ignore[attr-defined]
+            actual = actual.cpu()
+            expected = expected.cpu()
+
+        if actual.device != expected.device:
+            actual = actual.cpu()
+            expected = expected.cpu()
+
+        if actual.dtype != expected.dtype:
+            actual_dtype = actual.dtype
+            expected_dtype = expected.dtype
+            # For uint64, this is not sound in general, which is why promote_types doesn't
+            # allow it, but for easy testing, we're unlikely to get confused
+            # by large uint64 overflowing into negative int64
+            if actual_dtype in [torch.uint64, torch.uint32, torch.uint16]:
+                actual_dtype = torch.int64
+            if expected_dtype in [torch.uint64, torch.uint32, torch.uint16]:
+                expected_dtype = torch.int64
+            dtype = torch.promote_types(actual_dtype, expected_dtype)
+            actual = actual.to(dtype)
+            expected = expected.to(dtype)
+
+        if actual.layout != expected.layout:
+            # These checks are needed, since Tensor.to_dense() fails on tensors that are already strided
+            actual = actual.to_dense() if actual.layout != torch.strided else actual
+            expected = (
+                expected.to_dense() if expected.layout != torch.strided else expected
+            )
+
+        return actual, expected
+
+    def _compare_values(self, actual: torch.Tensor, expected: torch.Tensor) -> None:
+        if actual.is_quantized:
+            compare_fn = self._compare_quantized_values
+        elif actual.is_sparse:
+            compare_fn = self._compare_sparse_coo_values
+        elif actual.layout in {
+            torch.sparse_csr,
+            torch.sparse_csc,
+            torch.sparse_bsr,
+            torch.sparse_bsc,
+        }:
+            compare_fn = self._compare_sparse_compressed_values
+        else:
+            compare_fn = self._compare_regular_values_close
+
+        compare_fn(
+            actual, expected, rtol=self.rtol, atol=self.atol, equal_nan=self.equal_nan
+        )
+
+    def _compare_quantized_values(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        rtol: float,
+        atol: float,
+        equal_nan: bool,
+    ) -> None:
+        """Compares quantized tensors by comparing the :meth:`~torch.Tensor.dequantize`'d variants for closeness.
+
+        .. note::
+
+            A detailed discussion about why only the dequantized variant is checked for closeness rather than checking
+            the individual quantization parameters for closeness and the integer representation for equality can be
+            found in https://github.com/pytorch/pytorch/issues/68548.
+        """
+        return self._compare_regular_values_close(
+            actual.dequantize(),
+            expected.dequantize(),
+            rtol=rtol,
+            atol=atol,
+            equal_nan=equal_nan,
+            identifier=lambda default_identifier: f"Quantized {default_identifier.lower()}",
+        )
+
+    def _compare_sparse_coo_values(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        rtol: float,
+        atol: float,
+        equal_nan: bool,
+    ) -> None:
+        """Compares sparse COO tensors by comparing
+
+        - the number of sparse dimensions,
+        - the number of non-zero elements (nnz) for equality,
+        - the indices for equality, and
+        - the values for closeness.
+        """
+        if actual.sparse_dim() != expected.sparse_dim():
+            self._fail(
+                AssertionError,
+                (
+                    f"The number of sparse dimensions in sparse COO tensors does not match: "
+                    f"{actual.sparse_dim()} != {expected.sparse_dim()}"
+                ),
+            )
+
+        if actual._nnz() != expected._nnz():
+            self._fail(
+                AssertionError,
+                (
+                    f"The number of specified values in sparse COO tensors does not match: "
+                    f"{actual._nnz()} != {expected._nnz()}"
+                ),
+            )
+
+        self._compare_regular_values_equal(
+            actual._indices(),
+            expected._indices(),
+            identifier="Sparse COO indices",
+        )
+        self._compare_regular_values_close(
+            actual._values(),
+            expected._values(),
+            rtol=rtol,
+            atol=atol,
+            equal_nan=equal_nan,
+            identifier="Sparse COO values",
+        )
+
+    def _compare_sparse_compressed_values(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        rtol: float,
+        atol: float,
+        equal_nan: bool,
+    ) -> None:
+        """Compares sparse compressed tensors by comparing
+
+        - the number of non-zero elements (nnz) for equality,
+        - the plain indices for equality,
+        - the compressed indices for equality, and
+        - the values for closeness.
+        """
+        format_name, compressed_indices_method, plain_indices_method = {
+            torch.sparse_csr: (
+                "CSR",
+                torch.Tensor.crow_indices,
+                torch.Tensor.col_indices,
+            ),
+            torch.sparse_csc: (
+                "CSC",
+                torch.Tensor.ccol_indices,
+                torch.Tensor.row_indices,
+            ),
+            torch.sparse_bsr: (
+                "BSR",
+                torch.Tensor.crow_indices,
+                torch.Tensor.col_indices,
+            ),
+            torch.sparse_bsc: (
+                "BSC",
+                torch.Tensor.ccol_indices,
+                torch.Tensor.row_indices,
+            ),
+        }[actual.layout]
+
+        if actual._nnz() != expected._nnz():
+            self._fail(
+                AssertionError,
+                (
+                    f"The number of specified values in sparse {format_name} tensors does not match: "
+                    f"{actual._nnz()} != {expected._nnz()}"
+                ),
+            )
+
+        # Compressed and plain indices in the CSR / CSC / BSR / BSC sparse formates can be `torch.int32` _or_
+        # `torch.int64`. While the same dtype is enforced for the compressed and plain indices of a single tensor, it
+        # can be different between two tensors. Thus, we need to convert them to the same dtype, or the comparison will
+        # fail.
+        actual_compressed_indices = compressed_indices_method(actual)
+        expected_compressed_indices = compressed_indices_method(expected)
+        indices_dtype = torch.promote_types(
+            actual_compressed_indices.dtype, expected_compressed_indices.dtype
+        )
+
+        self._compare_regular_values_equal(
+            actual_compressed_indices.to(indices_dtype),
+            expected_compressed_indices.to(indices_dtype),
+            identifier=f"Sparse {format_name} {compressed_indices_method.__name__}",
+        )
+        self._compare_regular_values_equal(
+            plain_indices_method(actual).to(indices_dtype),
+            plain_indices_method(expected).to(indices_dtype),
+            identifier=f"Sparse {format_name} {plain_indices_method.__name__}",
+        )
+        self._compare_regular_values_close(
+            actual.values(),
+            expected.values(),
+            rtol=rtol,
+            atol=atol,
+            equal_nan=equal_nan,
+            identifier=f"Sparse {format_name} values",
+        )
+
+    def _compare_regular_values_equal(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        equal_nan: bool = False,
+        identifier: Optional[Union[str, Callable[[str], str]]] = None,
+    ) -> None:
+        """Checks if the values of two tensors are equal."""
+        self._compare_regular_values_close(
+            actual, expected, rtol=0, atol=0, equal_nan=equal_nan, identifier=identifier
+        )
+
+    def _compare_regular_values_close(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        rtol: float,
+        atol: float,
+        equal_nan: bool,
+        identifier: Optional[Union[str, Callable[[str], str]]] = None,
+    ) -> None:
+        """Checks if the values of two tensors are close up to a desired tolerance."""
+        matches = torch.isclose(
+            actual, expected, rtol=rtol, atol=atol, equal_nan=equal_nan
+        )
+        if torch.all(matches):
+            return
+
+        if actual.shape == torch.Size([]):
+            msg = make_scalar_mismatch_msg(
+                actual.item(),
+                expected.item(),
+                rtol=rtol,
+                atol=atol,
+                identifier=identifier,
+            )
+        else:
+            msg = make_tensor_mismatch_msg(
+                actual, expected, matches, rtol=rtol, atol=atol, identifier=identifier
+            )
+        self._fail(AssertionError, msg)
+
+    def extra_repr(self) -> Sequence[str]:
+        return (
+            "rtol",
+            "atol",
+            "equal_nan",
+            "check_device",
+            "check_dtype",
+            "check_layout",
+            "check_stride",
+        )
+
+
+def originate_pairs(
+    actual: Any,
+    expected: Any,
+    *,
+    pair_types: Sequence[Type[Pair]],
+    sequence_types: Tuple[Type, ...] = (collections.abc.Sequence,),
+    mapping_types: Tuple[Type, ...] = (collections.abc.Mapping,),
+    id: Tuple[Any, ...] = (),
+    **options: Any,
+) -> List[Pair]:
+    """Originates pairs from the individual inputs.
+
+    ``actual`` and ``expected`` can be possibly nested :class:`~collections.abc.Sequence`'s or
+    :class:`~collections.abc.Mapping`'s. In this case the pairs are originated by recursing through them.
+
+    Args:
+        actual (Any): Actual input.
+        expected (Any): Expected input.
+        pair_types (Sequence[Type[Pair]]): Sequence of pair types that will be tried to construct with the inputs.
+            First successful pair will be used.
+        sequence_types (Tuple[Type, ...]): Optional types treated as sequences that will be checked elementwise.
+        mapping_types (Tuple[Type, ...]): Optional types treated as mappings that will be checked elementwise.
+        id (Tuple[Any, ...]): Optional id of a pair that will be included in an error message.
+        **options (Any): Options passed to each pair during construction.
+
+    Raises:
+        ErrorMeta: With :class`AssertionError`, if the inputs are :class:`~collections.abc.Sequence`'s, but their
+            length does not match.
+        ErrorMeta: With :class`AssertionError`, if the inputs are :class:`~collections.abc.Mapping`'s, but their set of
+            keys do not match.
+        ErrorMeta: With :class`TypeError`, if no pair is able to handle the inputs.
+        ErrorMeta: With any expected exception that happens during the construction of a pair.
+
+    Returns:
+        (List[Pair]): Originated pairs.
+    """
+    # We explicitly exclude str's here since they are self-referential and would cause an infinite recursion loop:
+    # "a" == "a"[0][0]...
+    if (
+        isinstance(actual, sequence_types)
+        and not isinstance(actual, str)
+        and isinstance(expected, sequence_types)
+        and not isinstance(expected, str)
+    ):
+        actual_len = len(actual)
+        expected_len = len(expected)
+        if actual_len != expected_len:
+            raise ErrorMeta(
+                AssertionError,
+                f"The length of the sequences mismatch: {actual_len} != {expected_len}",
+                id=id,
+            )
+
+        pairs = []
+        for idx in range(actual_len):
+            pairs.extend(
+                originate_pairs(
+                    actual[idx],
+                    expected[idx],
+                    pair_types=pair_types,
+                    sequence_types=sequence_types,
+                    mapping_types=mapping_types,
+                    id=(*id, idx),
+                    **options,
+                )
+            )
+        return pairs
+
+    elif isinstance(actual, mapping_types) and isinstance(expected, mapping_types):
+        actual_keys = set(actual.keys())
+        expected_keys = set(expected.keys())
+        if actual_keys != expected_keys:
+            missing_keys = expected_keys - actual_keys
+            additional_keys = actual_keys - expected_keys
+            raise ErrorMeta(
+                AssertionError,
+                (
+                    f"The keys of the mappings do not match:\n"
+                    f"Missing keys in the actual mapping: {sorted(missing_keys)}\n"
+                    f"Additional keys in the actual mapping: {sorted(additional_keys)}"
+                ),
+                id=id,
+            )
+
+        keys: Collection = actual_keys
+        # Since the origination aborts after the first failure, we try to be deterministic
+        with contextlib.suppress(Exception):
+            keys = sorted(keys)
+
+        pairs = []
+        for key in keys:
+            pairs.extend(
+                originate_pairs(
+                    actual[key],
+                    expected[key],
+                    pair_types=pair_types,
+                    sequence_types=sequence_types,
+                    mapping_types=mapping_types,
+                    id=(*id, key),
+                    **options,
+                )
+            )
+        return pairs
+
+    else:
+        for pair_type in pair_types:
+            try:
+                return [pair_type(actual, expected, id=id, **options)]
+            # Raising an `UnsupportedInputs` during origination indicates that the pair type is not able to handle the
+            # inputs. Thus, we try the next pair type.
+            except UnsupportedInputs:
+                continue
+            # Raising an `ErrorMeta` during origination is the orderly way to abort and so we simply re-raise it. This
+            # is only in a separate branch, because the one below would also except it.
+            except ErrorMeta:
+                raise
+            # Raising any other exception during origination is unexpected and will give some extra information about
+            # what happened. If applicable, the exception should be expected in the future.
+            except Exception as error:
+                raise RuntimeError(
+                    f"Originating a {pair_type.__name__}() at item {''.join(str([item]) for item in id)} with\n\n"
+                    f"{type(actual).__name__}(): {actual}\n\n"
+                    f"and\n\n"
+                    f"{type(expected).__name__}(): {expected}\n\n"
+                    f"resulted in the unexpected exception above. "
+                    f"If you are a user and see this message during normal operation "
+                    "please file an issue at https://github.com/pytorch/pytorch/issues. "
+                    "If you are a developer and working on the comparison functions, "
+                    "please except the previous error and raise an expressive `ErrorMeta` instead."
+                ) from error
+        else:
+            raise ErrorMeta(
+                TypeError,
+                f"No comparison pair was able to handle inputs of type {type(actual)} and {type(expected)}.",
+                id=id,
+            )
+
+
+def not_close_error_metas(
+    actual: Any,
+    expected: Any,
+    *,
+    pair_types: Sequence[Type[Pair]] = (ObjectPair,),
+    sequence_types: Tuple[Type, ...] = (collections.abc.Sequence,),
+    mapping_types: Tuple[Type, ...] = (collections.abc.Mapping,),
+    **options: Any,
+) -> List[ErrorMeta]:
+    """Asserts that inputs are equal.
+
+    ``actual`` and ``expected`` can be possibly nested :class:`~collections.abc.Sequence`'s or
+    :class:`~collections.abc.Mapping`'s. In this case the comparison happens elementwise by recursing through them.
+
+    Args:
+        actual (Any): Actual input.
+        expected (Any): Expected input.
+        pair_types (Sequence[Type[Pair]]): Sequence of :class:`Pair` types that will be tried to construct with the
+            inputs. First successful pair will be used. Defaults to only using :class:`ObjectPair`.
+        sequence_types (Tuple[Type, ...]): Optional types treated as sequences that will be checked elementwise.
+        mapping_types (Tuple[Type, ...]): Optional types treated as mappings that will be checked elementwise.
+        **options (Any): Options passed to each pair during construction.
+    """
+    # Hide this function from `pytest`'s traceback
+    __tracebackhide__ = True
+
+    try:
+        pairs = originate_pairs(
+            actual,
+            expected,
+            pair_types=pair_types,
+            sequence_types=sequence_types,
+            mapping_types=mapping_types,
+            **options,
+        )
+    except ErrorMeta as error_meta:
+        # Explicitly raising from None to hide the internal traceback
+        raise error_meta.to_error() from None
+
+    error_metas: List[ErrorMeta] = []
+    for pair in pairs:
+        try:
+            pair.compare()
+        except ErrorMeta as error_meta:
+            error_metas.append(error_meta)
+        # Raising any exception besides `ErrorMeta` while comparing is unexpected and will give some extra information
+        # about what happened. If applicable, the exception should be expected in the future.
+        except Exception as error:
+            raise RuntimeError(
+                f"Comparing\n\n"
+                f"{pair}\n\n"
+                f"resulted in the unexpected exception above. "
+                f"If you are a user and see this message during normal operation "
+                "please file an issue at https://github.com/pytorch/pytorch/issues. "
+                "If you are a developer and working on the comparison functions, "
+                "please except the previous error and raise an expressive `ErrorMeta` instead."
+            ) from error
+
+    # [ErrorMeta Cycles]
+    # ErrorMeta objects in this list capture
+    # tracebacks that refer to the frame of this function.
+    # The local variable `error_metas` refers to the error meta
+    # objects, creating a reference cycle. Frames in the traceback
+    # would not get freed until cycle collection, leaking cuda memory in tests.
+    # We break the cycle by removing the reference to the error_meta objects
+    # from this frame as it returns.
+    error_metas = [error_metas]
+    return error_metas.pop()
+
+
+def assert_close(
+    actual: Any,
+    expected: Any,
+    *,
+    allow_subclasses: bool = True,
+    rtol: Optional[float] = None,
+    atol: Optional[float] = None,
+    equal_nan: bool = False,
+    check_device: bool = True,
+    check_dtype: bool = True,
+    check_layout: bool = True,
+    check_stride: bool = False,
+    msg: Optional[Union[str, Callable[[str], str]]] = None,
+):
+    r"""Asserts that ``actual`` and ``expected`` are close.
+
+    If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if
+
+    .. math::
+
+        \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert
+
+    Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are
+    only considered equal to each other if ``equal_nan`` is ``True``.
+
+    In addition, they are only considered close if they have the same
+
+    - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``),
+    - ``dtype`` (if ``check_dtype`` is ``True``),
+    - ``layout`` (if ``check_layout`` is ``True``), and
+    - stride (if ``check_stride`` is ``True``).
+
+    If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed.
+
+    If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are
+    checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR,
+    or ``ccol_indices``  and ``row_indices`` for CSC and BSC layouts, respectively,
+    are always checked for equality whereas the values are checked for closeness according to the definition above.
+
+    If ``actual`` and ``expected`` are quantized, they are considered close if they have the same
+    :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the
+    definition above.
+
+    ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which
+    :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types
+    have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s
+    or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all
+    their elements are considered close according to the above definition.
+
+    .. note::
+
+        Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e.
+        :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus,
+        Python scalars of different types can be checked, but require ``check_dtype=False``.
+
+    Args:
+        actual (Any): Actual input.
+        expected (Any): Expected input.
+        allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types
+            are allowed. Otherwise type equality is required.
+        rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
+            values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
+        atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
+            values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
+        equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal.
+        check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
+            :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
+            :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
+        check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
+            check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
+            :func:`torch.promote_types`) before being compared.
+        check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
+            check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
+            compared.
+        check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
+        msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during
+            the comparison. Can also passed as callable in which case it will be called with the generated message and
+            should return the new message.
+
+    Raises:
+        ValueError: If no :class:`torch.Tensor` can be constructed from an input.
+        ValueError: If only ``rtol`` or ``atol`` is specified.
+        AssertionError: If corresponding inputs are not Python scalars and are not directly related.
+        AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have
+            different types.
+        AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match.
+        AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match.
+        AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`.
+        AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same
+            :attr:`~torch.Tensor.layout`.
+        AssertionError: If only one of corresponding tensors is quantized.
+        AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s.
+        AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same
+            :attr:`~torch.Tensor.device`.
+        AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``.
+        AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride.
+        AssertionError: If the values of corresponding tensors are not close according to the definition above.
+
+    The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching
+    ``dtype``'s, the maximum of both tolerances is used.
+
+    +---------------------------+------------+----------+
+    | ``dtype``                 | ``rtol``   | ``atol`` |
+    +===========================+============+==========+
+    | :attr:`~torch.float16`    | ``1e-3``   | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.bfloat16`   | ``1.6e-2`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.float32`    | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.float64`    | ``1e-7``   | ``1e-7`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.complex32`  | ``1e-3``   | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.complex64`  | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.complex128` | ``1e-7``   | ``1e-7`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.quint8`     | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.quint2x4`   | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.quint4x2`   | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.qint8`      | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.qint32`     | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | other                     | ``0.0``    | ``0.0``  |
+    +---------------------------+------------+----------+
+
+    .. note::
+
+        :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged
+        to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might
+        define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default:
+
+        >>> import functools
+        >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0)
+        >>> assert_equal(1e-9, 1e-10)
+        Traceback (most recent call last):
+        ...
+        AssertionError: Scalars are not equal!
+        <BLANKLINE>
+        Expected 1e-10 but got 1e-09.
+        Absolute difference: 9.000000000000001e-10
+        Relative difference: 9.0
+
+    Examples:
+        >>> # tensor to tensor comparison
+        >>> expected = torch.tensor([1e0, 1e-1, 1e-2])
+        >>> actual = torch.acos(torch.cos(expected))
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> # scalar to scalar comparison
+        >>> import math
+        >>> expected = math.sqrt(2.0)
+        >>> actual = 2.0 / math.sqrt(2.0)
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> # numpy array to numpy array comparison
+        >>> import numpy as np
+        >>> expected = np.array([1e0, 1e-1, 1e-2])
+        >>> actual = np.arccos(np.cos(expected))
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> # sequence to sequence comparison
+        >>> import numpy as np
+        >>> # The types of the sequences do not have to match. They only have to have the same
+        >>> # length and their elements have to match.
+        >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)]
+        >>> actual = tuple(expected)
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> # mapping to mapping comparison
+        >>> from collections import OrderedDict
+        >>> import numpy as np
+        >>> foo = torch.tensor(1.0)
+        >>> bar = 2.0
+        >>> baz = np.array(3.0)
+        >>> # The types and a possible ordering of mappings do not have to match. They only
+        >>> # have to have the same set of keys and their elements have to match.
+        >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)])
+        >>> actual = {"baz": baz, "bar": bar, "foo": foo}
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> expected = torch.tensor([1.0, 2.0, 3.0])
+        >>> actual = expected.clone()
+        >>> # By default, directly related instances can be compared
+        >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected)
+        >>> # This check can be made more strict with allow_subclasses=False
+        >>> torch.testing.assert_close(
+        ...     torch.nn.Parameter(actual), expected, allow_subclasses=False
+        ... )
+        Traceback (most recent call last):
+        ...
+        TypeError: No comparison pair was able to handle inputs of type
+        <class 'torch.nn.parameter.Parameter'> and <class 'torch.Tensor'>.
+        >>> # If the inputs are not directly related, they are never considered close
+        >>> torch.testing.assert_close(actual.numpy(), expected)
+        Traceback (most recent call last):
+        ...
+        TypeError: No comparison pair was able to handle inputs of type <class 'numpy.ndarray'>
+        and <class 'torch.Tensor'>.
+        >>> # Exceptions to these rules are Python scalars. They can be checked regardless of
+        >>> # their type if check_dtype=False.
+        >>> torch.testing.assert_close(1.0, 1, check_dtype=False)
+
+        >>> # NaN != NaN by default.
+        >>> expected = torch.tensor(float("Nan"))
+        >>> actual = expected.clone()
+        >>> torch.testing.assert_close(actual, expected)
+        Traceback (most recent call last):
+        ...
+        AssertionError: Scalars are not close!
+        <BLANKLINE>
+        Expected nan but got nan.
+        Absolute difference: nan (up to 1e-05 allowed)
+        Relative difference: nan (up to 1.3e-06 allowed)
+        >>> torch.testing.assert_close(actual, expected, equal_nan=True)
+
+        >>> expected = torch.tensor([1.0, 2.0, 3.0])
+        >>> actual = torch.tensor([1.0, 4.0, 5.0])
+        >>> # The default error message can be overwritten.
+        >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!")
+        Traceback (most recent call last):
+        ...
+        AssertionError: Argh, the tensors are not close!
+        >>> # If msg is a callable, it can be used to augment the generated message with
+        >>> # extra information
+        >>> torch.testing.assert_close(
+        ...     actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter"
+        ... )
+        Traceback (most recent call last):
+        ...
+        AssertionError: Header
+        <BLANKLINE>
+        Tensor-likes are not close!
+        <BLANKLINE>
+        Mismatched elements: 2 / 3 (66.7%)
+        Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed)
+        Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed)
+        <BLANKLINE>
+        Footer
+    """
+    # Hide this function from `pytest`'s traceback
+    __tracebackhide__ = True
+
+    error_metas = not_close_error_metas(
+        actual,
+        expected,
+        pair_types=(
+            NonePair,
+            BooleanPair,
+            NumberPair,
+            TensorLikePair,
+        ),
+        allow_subclasses=allow_subclasses,
+        rtol=rtol,
+        atol=atol,
+        equal_nan=equal_nan,
+        check_device=check_device,
+        check_dtype=check_dtype,
+        check_layout=check_layout,
+        check_stride=check_stride,
+        msg=msg,
+    )
+
+    if error_metas:
+        # TODO: compose all metas into one AssertionError
+        raise error_metas[0].to_error(msg)
+
+
+def assert_allclose(
+    actual: Any,
+    expected: Any,
+    rtol: Optional[float] = None,
+    atol: Optional[float] = None,
+    equal_nan: bool = True,
+    msg: str = "",
+) -> None:
+    """
+    .. warning::
+
+       :func:`torch.testing.assert_allclose` is deprecated since ``1.12`` and will be removed in a future release.
+       Please use :func:`torch.testing.assert_close` instead. You can find detailed upgrade instructions
+       `here <https://github.com/pytorch/pytorch/issues/61844>`_.
+    """
+    warnings.warn(
+        "`torch.testing.assert_allclose()` is deprecated since 1.12 and will be removed in a future release. "
+        "Please use `torch.testing.assert_close()` instead. "
+        "You can find detailed upgrade instructions in https://github.com/pytorch/pytorch/issues/61844.",
+        FutureWarning,
+        stacklevel=2,
+    )
+
+    if not isinstance(actual, torch.Tensor):
+        actual = torch.tensor(actual)
+    if not isinstance(expected, torch.Tensor):
+        expected = torch.tensor(expected, dtype=actual.dtype)
+
+    if rtol is None and atol is None:
+        rtol, atol = default_tolerances(
+            actual,
+            expected,
+            dtype_precisions={
+                torch.float16: (1e-3, 1e-3),
+                torch.float32: (1e-4, 1e-5),
+                torch.float64: (1e-5, 1e-8),
+            },
+        )
+
+    torch.testing.assert_close(
+        actual,
+        expected,
+        rtol=rtol,
+        atol=atol,
+        equal_nan=equal_nan,
+        check_device=True,
+        check_dtype=False,
+        check_stride=False,
+        msg=msg or None,
+    )

shared/package-files/31/31ddedd22e89ab666068c076cb95a18d5c62e82350fc7173a0d4e87c01ebb1cc/blob

@@ -0,0 +1,47 @@
+# mypy: ignore-errors
+
+"""
+Python polyfills for common builtins.
+"""
+import math
+
+import torch
+
+
+def all(iterator):
+    for elem in iterator:
+        if not elem:
+            return False
+    return True
+
+
+def any(iterator):
+    for elem in iterator:
+        if elem:
+            return True
+    return False
+
+
+def index(iterator, item, start=0, end=None):
+    for i, elem in enumerate(list(iterator))[start:end]:
+        if item == elem:
+            return i
+    # This will not run in dynamo
+    raise ValueError(f"{item} is not in {type(iterator)}")
+
+
+def repeat(item, count):
+    for i in range(count):
+        yield item
+
+
+def radians(x):
+    return math.pi / 180.0 * x
+
+
+def accumulate_grad(x, new_grad):
+    new_grad = torch.clone(new_grad)
+    if x.grad is None:
+        x.grad = new_grad
+    else:
+        x.grad.add_(new_grad)

shared/package-files/31/31e3a1164bfdd402e278a26d187aa487180eeca241cf9de607cb51718097df19/blob

@@ -0,0 +1,158 @@
+"""
+Cuthill-McKee ordering of graph nodes to produce sparse matrices
+"""
+from collections import deque
+from operator import itemgetter
+
+import networkx as nx
+
+from ..utils import arbitrary_element
+
+__all__ = ["cuthill_mckee_ordering", "reverse_cuthill_mckee_ordering"]
+
+
+def cuthill_mckee_ordering(G, heuristic=None):
+    """Generate an ordering (permutation) of the graph nodes to make
+    a sparse matrix.
+
+    Uses the Cuthill-McKee heuristic (based on breadth-first search) [1]_.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    heuristic : function, optional
+      Function to choose starting node for RCM algorithm.  If None
+      a node from a pseudo-peripheral pair is used.  A user-defined function
+      can be supplied that takes a graph object and returns a single node.
+
+    Returns
+    -------
+    nodes : generator
+       Generator of nodes in Cuthill-McKee ordering.
+
+    Examples
+    --------
+    >>> from networkx.utils import cuthill_mckee_ordering
+    >>> G = nx.path_graph(4)
+    >>> rcm = list(cuthill_mckee_ordering(G))
+    >>> A = nx.adjacency_matrix(G, nodelist=rcm)
+
+    Smallest degree node as heuristic function:
+
+    >>> def smallest_degree(G):
+    ...     return min(G, key=G.degree)
+    >>> rcm = list(cuthill_mckee_ordering(G, heuristic=smallest_degree))
+
+
+    See Also
+    --------
+    reverse_cuthill_mckee_ordering
+
+    Notes
+    -----
+    The optimal solution the bandwidth reduction is NP-complete [2]_.
+
+
+    References
+    ----------
+    .. [1] E. Cuthill and J. McKee.
+       Reducing the bandwidth of sparse symmetric matrices,
+       In Proc. 24th Nat. Conf. ACM, pages 157-172, 1969.
+       http://doi.acm.org/10.1145/800195.805928
+    .. [2]  Steven S. Skiena. 1997. The Algorithm Design Manual.
+       Springer-Verlag New York, Inc., New York, NY, USA.
+    """
+    for c in nx.connected_components(G):
+        yield from connected_cuthill_mckee_ordering(G.subgraph(c), heuristic)
+
+
+def reverse_cuthill_mckee_ordering(G, heuristic=None):
+    """Generate an ordering (permutation) of the graph nodes to make
+    a sparse matrix.
+
+    Uses the reverse Cuthill-McKee heuristic (based on breadth-first search)
+    [1]_.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    heuristic : function, optional
+      Function to choose starting node for RCM algorithm.  If None
+      a node from a pseudo-peripheral pair is used.  A user-defined function
+      can be supplied that takes a graph object and returns a single node.
+
+    Returns
+    -------
+    nodes : generator
+       Generator of nodes in reverse Cuthill-McKee ordering.
+
+    Examples
+    --------
+    >>> from networkx.utils import reverse_cuthill_mckee_ordering
+    >>> G = nx.path_graph(4)
+    >>> rcm = list(reverse_cuthill_mckee_ordering(G))
+    >>> A = nx.adjacency_matrix(G, nodelist=rcm)
+
+    Smallest degree node as heuristic function:
+
+    >>> def smallest_degree(G):
+    ...     return min(G, key=G.degree)
+    >>> rcm = list(reverse_cuthill_mckee_ordering(G, heuristic=smallest_degree))
+
+
+    See Also
+    --------
+    cuthill_mckee_ordering
+
+    Notes
+    -----
+    The optimal solution the bandwidth reduction is NP-complete [2]_.
+
+    References
+    ----------
+    .. [1] E. Cuthill and J. McKee.
+       Reducing the bandwidth of sparse symmetric matrices,
+       In Proc. 24th Nat. Conf. ACM, pages 157-72, 1969.
+       http://doi.acm.org/10.1145/800195.805928
+    .. [2]  Steven S. Skiena. 1997. The Algorithm Design Manual.
+       Springer-Verlag New York, Inc., New York, NY, USA.
+    """
+    return reversed(list(cuthill_mckee_ordering(G, heuristic=heuristic)))
+
+
+def connected_cuthill_mckee_ordering(G, heuristic=None):
+    # the cuthill mckee algorithm for connected graphs
+    if heuristic is None:
+        start = pseudo_peripheral_node(G)
+    else:
+        start = heuristic(G)
+    visited = {start}
+    queue = deque([start])
+    while queue:
+        parent = queue.popleft()
+        yield parent
+        nd = sorted(G.degree(set(G[parent]) - visited), key=itemgetter(1))
+        children = [n for n, d in nd]
+        visited.update(children)
+        queue.extend(children)
+
+
+def pseudo_peripheral_node(G):
+    # helper for cuthill-mckee to find a node in a "pseudo peripheral pair"
+    # to use as good starting node
+    u = arbitrary_element(G)
+    lp = 0
+    v = u
+    while True:
+        spl = dict(nx.shortest_path_length(G, v))
+        l = max(spl.values())
+        if l <= lp:
+            break
+        lp = l
+        farthest = (n for n, dist in spl.items() if dist == l)
+        v, deg = min(G.degree(farthest), key=itemgetter(1))
+    return v

shared/package-files/31/31e6182e61583cd1b8d31359b26ecead2dba2ae003a7714c16afc1795bdbeceb/blob

@@ -0,0 +1,1400 @@
+# FontDame-to-FontTools for OpenType Layout tables
+#
+# Source language spec is available at:
+# http://monotype.github.io/OpenType_Table_Source/otl_source.html
+# https://github.com/Monotype/OpenType_Table_Source/
+
+from fontTools import ttLib
+from fontTools.ttLib.tables._c_m_a_p import cmap_classes
+from fontTools.ttLib.tables import otTables as ot
+from fontTools.ttLib.tables.otBase import ValueRecord, valueRecordFormatDict
+from fontTools.otlLib import builder as otl
+from contextlib import contextmanager
+from fontTools.ttLib import newTable
+from fontTools.feaLib.lookupDebugInfo import LOOKUP_DEBUG_ENV_VAR, LOOKUP_DEBUG_INFO_KEY
+from operator import setitem
+import os
+import logging
+
+
+class MtiLibError(Exception):
+    pass
+
+
+class ReferenceNotFoundError(MtiLibError):
+    pass
+
+
+class FeatureNotFoundError(ReferenceNotFoundError):
+    pass
+
+
+class LookupNotFoundError(ReferenceNotFoundError):
+    pass
+
+
+log = logging.getLogger("fontTools.mtiLib")
+
+
+def makeGlyph(s):
+    if s[:2] in ["U ", "u "]:
+        return ttLib.TTFont._makeGlyphName(int(s[2:], 16))
+    elif s[:2] == "# ":
+        return "glyph%.5d" % int(s[2:])
+    assert s.find(" ") < 0, "Space found in glyph name: %s" % s
+    assert s, "Glyph name is empty"
+    return s
+
+
+def makeGlyphs(l):
+    return [makeGlyph(g) for g in l]
+
+
+def mapLookup(sym, mapping):
+    # Lookups are addressed by name.  So resolved them using a map if available.
+    # Fallback to parsing as lookup index if a map isn't provided.
+    if mapping is not None:
+        try:
+            idx = mapping[sym]
+        except KeyError:
+            raise LookupNotFoundError(sym)
+    else:
+        idx = int(sym)
+    return idx
+
+
+def mapFeature(sym, mapping):
+    # Features are referenced by index according the spec.  So, if symbol is an
+    # integer, use it directly.  Otherwise look up in the map if provided.
+    try:
+        idx = int(sym)
+    except ValueError:
+        try:
+            idx = mapping[sym]
+        except KeyError:
+            raise FeatureNotFoundError(sym)
+    return idx
+
+
+def setReference(mapper, mapping, sym, setter, collection, key):
+    try:
+        mapped = mapper(sym, mapping)
+    except ReferenceNotFoundError as e:
+        try:
+            if mapping is not None:
+                mapping.addDeferredMapping(
+                    lambda ref: setter(collection, key, ref), sym, e
+                )
+                return
+        except AttributeError:
+            pass
+        raise
+    setter(collection, key, mapped)
+
+
+class DeferredMapping(dict):
+    def __init__(self):
+        self._deferredMappings = []
+
+    def addDeferredMapping(self, setter, sym, e):
+        log.debug("Adding deferred mapping for symbol '%s' %s", sym, type(e).__name__)
+        self._deferredMappings.append((setter, sym, e))
+
+    def applyDeferredMappings(self):
+        for setter, sym, e in self._deferredMappings:
+            log.debug(
+                "Applying deferred mapping for symbol '%s' %s", sym, type(e).__name__
+            )
+            try:
+                mapped = self[sym]
+            except KeyError:
+                raise e
+            setter(mapped)
+            log.debug("Set to %s", mapped)
+        self._deferredMappings = []
+
+
+def parseScriptList(lines, featureMap=None):
+    self = ot.ScriptList()
+    records = []
+    with lines.between("script table"):
+        for line in lines:
+            while len(line) < 4:
+                line.append("")
+            scriptTag, langSysTag, defaultFeature, features = line
+            log.debug("Adding script %s language-system %s", scriptTag, langSysTag)
+
+            langSys = ot.LangSys()
+            langSys.LookupOrder = None
+            if defaultFeature:
+                setReference(
+                    mapFeature,
+                    featureMap,
+                    defaultFeature,
+                    setattr,
+                    langSys,
+                    "ReqFeatureIndex",
+                )
+            else:
+                langSys.ReqFeatureIndex = 0xFFFF
+            syms = stripSplitComma(features)
+            langSys.FeatureIndex = theList = [3] * len(syms)
+            for i, sym in enumerate(syms):
+                setReference(mapFeature, featureMap, sym, setitem, theList, i)
+            langSys.FeatureCount = len(langSys.FeatureIndex)
+
+            script = [s for s in records if s.ScriptTag == scriptTag]
+            if script:
+                script = script[0].Script
+            else:
+                scriptRec = ot.ScriptRecord()
+                scriptRec.ScriptTag = scriptTag + " " * (4 - len(scriptTag))
+                scriptRec.Script = ot.Script()
+                records.append(scriptRec)
+                script = scriptRec.Script
+                script.DefaultLangSys = None
+                script.LangSysRecord = []
+                script.LangSysCount = 0
+
+            if langSysTag == "default":
+                script.DefaultLangSys = langSys
+            else:
+                langSysRec = ot.LangSysRecord()
+                langSysRec.LangSysTag = langSysTag + " " * (4 - len(langSysTag))
+                langSysRec.LangSys = langSys
+                script.LangSysRecord.append(langSysRec)
+                script.LangSysCount = len(script.LangSysRecord)
+
+    for script in records:
+        script.Script.LangSysRecord = sorted(
+            script.Script.LangSysRecord, key=lambda rec: rec.LangSysTag
+        )
+    self.ScriptRecord = sorted(records, key=lambda rec: rec.ScriptTag)
+    self.ScriptCount = len(self.ScriptRecord)
+    return self
+
+
+def parseFeatureList(lines, lookupMap=None, featureMap=None):
+    self = ot.FeatureList()
+    self.FeatureRecord = []
+    with lines.between("feature table"):
+        for line in lines:
+            name, featureTag, lookups = line
+            if featureMap is not None:
+                assert name not in featureMap, "Duplicate feature name: %s" % name
+                featureMap[name] = len(self.FeatureRecord)
+            # If feature name is integer, make sure it matches its index.
+            try:
+                assert int(name) == len(self.FeatureRecord), "%d %d" % (
+                    name,
+                    len(self.FeatureRecord),
+                )
+            except ValueError:
+                pass
+            featureRec = ot.FeatureRecord()
+            featureRec.FeatureTag = featureTag
+            featureRec.Feature = ot.Feature()
+            self.FeatureRecord.append(featureRec)
+            feature = featureRec.Feature
+            feature.FeatureParams = None
+            syms = stripSplitComma(lookups)
+            feature.LookupListIndex = theList = [None] * len(syms)
+            for i, sym in enumerate(syms):
+                setReference(mapLookup, lookupMap, sym, setitem, theList, i)
+            feature.LookupCount = len(feature.LookupListIndex)
+
+    self.FeatureCount = len(self.FeatureRecord)
+    return self
+
+
+def parseLookupFlags(lines):
+    flags = 0
+    filterset = None
+    allFlags = [
+        "righttoleft",
+        "ignorebaseglyphs",
+        "ignoreligatures",
+        "ignoremarks",
+        "markattachmenttype",
+        "markfiltertype",
+    ]
+    while lines.peeks()[0].lower() in allFlags:
+        line = next(lines)
+        flag = {
+            "righttoleft": 0x0001,
+            "ignorebaseglyphs": 0x0002,
+            "ignoreligatures": 0x0004,
+            "ignoremarks": 0x0008,
+        }.get(line[0].lower())
+        if flag:
+            assert line[1].lower() in ["yes", "no"], line[1]
+            if line[1].lower() == "yes":
+                flags |= flag
+            continue
+        if line[0].lower() == "markattachmenttype":
+            flags |= int(line[1]) << 8
+            continue
+        if line[0].lower() == "markfiltertype":
+            flags |= 0x10
+            filterset = int(line[1])
+    return flags, filterset
+
+
+def parseSingleSubst(lines, font, _lookupMap=None):
+    mapping = {}
+    for line in lines:
+        assert len(line) == 2, line
+        line = makeGlyphs(line)
+        mapping[line[0]] = line[1]
+    return otl.buildSingleSubstSubtable(mapping)
+
+
+def parseMultiple(lines, font, _lookupMap=None):
+    mapping = {}
+    for line in lines:
+        line = makeGlyphs(line)
+        mapping[line[0]] = line[1:]
+    return otl.buildMultipleSubstSubtable(mapping)
+
+
+def parseAlternate(lines, font, _lookupMap=None):
+    mapping = {}
+    for line in lines:
+        line = makeGlyphs(line)
+        mapping[line[0]] = line[1:]
+    return otl.buildAlternateSubstSubtable(mapping)
+
+
+def parseLigature(lines, font, _lookupMap=None):
+    mapping = {}
+    for line in lines:
+        assert len(line) >= 2, line
+        line = makeGlyphs(line)
+        mapping[tuple(line[1:])] = line[0]
+    return otl.buildLigatureSubstSubtable(mapping)
+
+
+def parseSinglePos(lines, font, _lookupMap=None):
+    values = {}
+    for line in lines:
+        assert len(line) == 3, line
+        w = line[0].title().replace(" ", "")
+        assert w in valueRecordFormatDict
+        g = makeGlyph(line[1])
+        v = int(line[2])
+        if g not in values:
+            values[g] = ValueRecord()
+        assert not hasattr(values[g], w), (g, w)
+        setattr(values[g], w, v)
+    return otl.buildSinglePosSubtable(values, font.getReverseGlyphMap())
+
+
+def parsePair(lines, font, _lookupMap=None):
+    self = ot.PairPos()
+    self.ValueFormat1 = self.ValueFormat2 = 0
+    typ = lines.peeks()[0].split()[0].lower()
+    if typ in ("left", "right"):
+        self.Format = 1
+        values = {}
+        for line in lines:
+            assert len(line) == 4, line
+            side = line[0].split()[0].lower()
+            assert side in ("left", "right"), side
+            what = line[0][len(side) :].title().replace(" ", "")
+            mask = valueRecordFormatDict[what][0]
+            glyph1, glyph2 = makeGlyphs(line[1:3])
+            value = int(line[3])
+            if not glyph1 in values:
+                values[glyph1] = {}
+            if not glyph2 in values[glyph1]:
+                values[glyph1][glyph2] = (ValueRecord(), ValueRecord())
+            rec2 = values[glyph1][glyph2]
+            if side == "left":
+                self.ValueFormat1 |= mask
+                vr = rec2[0]
+            else:
+                self.ValueFormat2 |= mask
+                vr = rec2[1]
+            assert not hasattr(vr, what), (vr, what)
+            setattr(vr, what, value)
+        self.Coverage = makeCoverage(set(values.keys()), font)
+        self.PairSet = []
+        for glyph1 in self.Coverage.glyphs:
+            values1 = values[glyph1]
+            pairset = ot.PairSet()
+            records = pairset.PairValueRecord = []
+            for glyph2 in sorted(values1.keys(), key=font.getGlyphID):
+                values2 = values1[glyph2]
+                pair = ot.PairValueRecord()
+                pair.SecondGlyph = glyph2
+                pair.Value1 = values2[0]
+                pair.Value2 = values2[1] if self.ValueFormat2 else None
+                records.append(pair)
+            pairset.PairValueCount = len(pairset.PairValueRecord)
+            self.PairSet.append(pairset)
+        self.PairSetCount = len(self.PairSet)
+    elif typ.endswith("class"):
+        self.Format = 2
+        classDefs = [None, None]
+        while lines.peeks()[0].endswith("class definition begin"):
+            typ = lines.peek()[0][: -len("class definition begin")].lower()
+            idx, klass = {
+                "first": (0, ot.ClassDef1),
+                "second": (1, ot.ClassDef2),
+            }[typ]
+            assert classDefs[idx] is None
+            classDefs[idx] = parseClassDef(lines, font, klass=klass)
+        self.ClassDef1, self.ClassDef2 = classDefs
+        self.Class1Count, self.Class2Count = (
+            1 + max(c.classDefs.values()) for c in classDefs
+        )
+        self.Class1Record = [ot.Class1Record() for i in range(self.Class1Count)]
+        for rec1 in self.Class1Record:
+            rec1.Class2Record = [ot.Class2Record() for j in range(self.Class2Count)]
+            for rec2 in rec1.Class2Record:
+                rec2.Value1 = ValueRecord()
+                rec2.Value2 = ValueRecord()
+        for line in lines:
+            assert len(line) == 4, line
+            side = line[0].split()[0].lower()
+            assert side in ("left", "right"), side
+            what = line[0][len(side) :].title().replace(" ", "")
+            mask = valueRecordFormatDict[what][0]
+            class1, class2, value = (int(x) for x in line[1:4])
+            rec2 = self.Class1Record[class1].Class2Record[class2]
+            if side == "left":
+                self.ValueFormat1 |= mask
+                vr = rec2.Value1
+            else:
+                self.ValueFormat2 |= mask
+                vr = rec2.Value2
+            assert not hasattr(vr, what), (vr, what)
+            setattr(vr, what, value)
+        for rec1 in self.Class1Record:
+            for rec2 in rec1.Class2Record:
+                rec2.Value1 = ValueRecord(self.ValueFormat1, rec2.Value1)
+                rec2.Value2 = (
+                    ValueRecord(self.ValueFormat2, rec2.Value2)
+                    if self.ValueFormat2
+                    else None
+                )
+
+        self.Coverage = makeCoverage(set(self.ClassDef1.classDefs.keys()), font)
+    else:
+        assert 0, typ
+    return self
+
+
+def parseKernset(lines, font, _lookupMap=None):
+    typ = lines.peeks()[0].split()[0].lower()
+    if typ in ("left", "right"):
+        with lines.until(
+            ("firstclass definition begin", "secondclass definition begin")
+        ):
+            return parsePair(lines, font)
+    return parsePair(lines, font)
+
+
+def makeAnchor(data, klass=ot.Anchor):
+    assert len(data) <= 2
+    anchor = klass()
+    anchor.Format = 1
+    anchor.XCoordinate, anchor.YCoordinate = intSplitComma(data[0])
+    if len(data) > 1 and data[1] != "":
+        anchor.Format = 2
+        anchor.AnchorPoint = int(data[1])
+    return anchor
+
+
+def parseCursive(lines, font, _lookupMap=None):
+    records = {}
+    for line in lines:
+        assert len(line) in [3, 4], line
+        idx, klass = {
+            "entry": (0, ot.EntryAnchor),
+            "exit": (1, ot.ExitAnchor),
+        }[line[0]]
+        glyph = makeGlyph(line[1])
+        if glyph not in records:
+            records[glyph] = [None, None]
+        assert records[glyph][idx] is None, (glyph, idx)
+        records[glyph][idx] = makeAnchor(line[2:], klass)
+    return otl.buildCursivePosSubtable(records, font.getReverseGlyphMap())
+
+
+def makeMarkRecords(data, coverage, c):
+    records = []
+    for glyph in coverage.glyphs:
+        klass, anchor = data[glyph]
+        record = c.MarkRecordClass()
+        record.Class = klass
+        setattr(record, c.MarkAnchor, anchor)
+        records.append(record)
+    return records
+
+
+def makeBaseRecords(data, coverage, c, classCount):
+    records = []
+    idx = {}
+    for glyph in coverage.glyphs:
+        idx[glyph] = len(records)
+        record = c.BaseRecordClass()
+        anchors = [None] * classCount
+        setattr(record, c.BaseAnchor, anchors)
+        records.append(record)
+    for (glyph, klass), anchor in data.items():
+        record = records[idx[glyph]]
+        anchors = getattr(record, c.BaseAnchor)
+        assert anchors[klass] is None, (glyph, klass)
+        anchors[klass] = anchor
+    return records
+
+
+def makeLigatureRecords(data, coverage, c, classCount):
+    records = [None] * len(coverage.glyphs)
+    idx = {g: i for i, g in enumerate(coverage.glyphs)}
+
+    for (glyph, klass, compIdx, compCount), anchor in data.items():
+        record = records[idx[glyph]]
+        if record is None:
+            record = records[idx[glyph]] = ot.LigatureAttach()
+            record.ComponentCount = compCount
+            record.ComponentRecord = [ot.ComponentRecord() for i in range(compCount)]
+            for compRec in record.ComponentRecord:
+                compRec.LigatureAnchor = [None] * classCount
+        assert record.ComponentCount == compCount, (
+            glyph,
+            record.ComponentCount,
+            compCount,
+        )
+
+        anchors = record.ComponentRecord[compIdx - 1].LigatureAnchor
+        assert anchors[klass] is None, (glyph, compIdx, klass)
+        anchors[klass] = anchor
+    return records
+
+
+def parseMarkToSomething(lines, font, c):
+    self = c.Type()
+    self.Format = 1
+    markData = {}
+    baseData = {}
+    Data = {
+        "mark": (markData, c.MarkAnchorClass),
+        "base": (baseData, c.BaseAnchorClass),
+        "ligature": (baseData, c.BaseAnchorClass),
+    }
+    maxKlass = 0
+    for line in lines:
+        typ = line[0]
+        assert typ in ("mark", "base", "ligature")
+        glyph = makeGlyph(line[1])
+        data, anchorClass = Data[typ]
+        extraItems = 2 if typ == "ligature" else 0
+        extras = tuple(int(i) for i in line[2 : 2 + extraItems])
+        klass = int(line[2 + extraItems])
+        anchor = makeAnchor(line[3 + extraItems :], anchorClass)
+        if typ == "mark":
+            key, value = glyph, (klass, anchor)
+        else:
+            key, value = ((glyph, klass) + extras), anchor
+        assert key not in data, key
+        data[key] = value
+        maxKlass = max(maxKlass, klass)
+
+    # Mark
+    markCoverage = makeCoverage(set(markData.keys()), font, c.MarkCoverageClass)
+    markArray = c.MarkArrayClass()
+    markRecords = makeMarkRecords(markData, markCoverage, c)
+    setattr(markArray, c.MarkRecord, markRecords)
+    setattr(markArray, c.MarkCount, len(markRecords))
+    setattr(self, c.MarkCoverage, markCoverage)
+    setattr(self, c.MarkArray, markArray)
+    self.ClassCount = maxKlass + 1
+
+    # Base
+    self.classCount = 0 if not baseData else 1 + max(k[1] for k, v in baseData.items())
+    baseCoverage = makeCoverage(
+        set([k[0] for k in baseData.keys()]), font, c.BaseCoverageClass
+    )
+    baseArray = c.BaseArrayClass()
+    if c.Base == "Ligature":
+        baseRecords = makeLigatureRecords(baseData, baseCoverage, c, self.classCount)
+    else:
+        baseRecords = makeBaseRecords(baseData, baseCoverage, c, self.classCount)
+    setattr(baseArray, c.BaseRecord, baseRecords)
+    setattr(baseArray, c.BaseCount, len(baseRecords))
+    setattr(self, c.BaseCoverage, baseCoverage)
+    setattr(self, c.BaseArray, baseArray)
+
+    return self
+
+
+class MarkHelper(object):
+    def __init__(self):
+        for Which in ("Mark", "Base"):
+            for What in ("Coverage", "Array", "Count", "Record", "Anchor"):
+                key = Which + What
+                if Which == "Mark" and What in ("Count", "Record", "Anchor"):
+                    value = key
+                else:
+                    value = getattr(self, Which) + What
+                if value == "LigatureRecord":
+                    value = "LigatureAttach"
+                setattr(self, key, value)
+                if What != "Count":
+                    klass = getattr(ot, value)
+                    setattr(self, key + "Class", klass)
+
+
+class MarkToBaseHelper(MarkHelper):
+    Mark = "Mark"
+    Base = "Base"
+    Type = ot.MarkBasePos
+
+
+class MarkToMarkHelper(MarkHelper):
+    Mark = "Mark1"
+    Base = "Mark2"
+    Type = ot.MarkMarkPos
+
+
+class MarkToLigatureHelper(MarkHelper):
+    Mark = "Mark"
+    Base = "Ligature"
+    Type = ot.MarkLigPos
+
+
+def parseMarkToBase(lines, font, _lookupMap=None):
+    return parseMarkToSomething(lines, font, MarkToBaseHelper())
+
+
+def parseMarkToMark(lines, font, _lookupMap=None):
+    return parseMarkToSomething(lines, font, MarkToMarkHelper())
+
+
+def parseMarkToLigature(lines, font, _lookupMap=None):
+    return parseMarkToSomething(lines, font, MarkToLigatureHelper())
+
+
+def stripSplitComma(line):
+    return [s.strip() for s in line.split(",")] if line else []
+
+
+def intSplitComma(line):
+    return [int(i) for i in line.split(",")] if line else []
+
+
+# Copied from fontTools.subset
+class ContextHelper(object):
+    def __init__(self, klassName, Format):
+        if klassName.endswith("Subst"):
+            Typ = "Sub"
+            Type = "Subst"
+        else:
+            Typ = "Pos"
+            Type = "Pos"
+        if klassName.startswith("Chain"):
+            Chain = "Chain"
+            InputIdx = 1
+            DataLen = 3
+        else:
+            Chain = ""
+            InputIdx = 0
+            DataLen = 1
+        ChainTyp = Chain + Typ
+
+        self.Typ = Typ
+        self.Type = Type
+        self.Chain = Chain
+        self.ChainTyp = ChainTyp
+        self.InputIdx = InputIdx
+        self.DataLen = DataLen
+
+        self.LookupRecord = Type + "LookupRecord"
+
+        if Format == 1:
+            Coverage = lambda r: r.Coverage
+            ChainCoverage = lambda r: r.Coverage
+            ContextData = lambda r: (None,)
+            ChainContextData = lambda r: (None, None, None)
+            SetContextData = None
+            SetChainContextData = None
+            RuleData = lambda r: (r.Input,)
+            ChainRuleData = lambda r: (r.Backtrack, r.Input, r.LookAhead)
+
+            def SetRuleData(r, d):
+                (r.Input,) = d
+                (r.GlyphCount,) = (len(x) + 1 for x in d)
+
+            def ChainSetRuleData(r, d):
+                r.Backtrack, r.Input, r.LookAhead = d
+                (
+                    r.BacktrackGlyphCount,
+                    r.InputGlyphCount,
+                    r.LookAheadGlyphCount,
+                ) = (len(d[0]), len(d[1]) + 1, len(d[2]))
+
+        elif Format == 2:
+            Coverage = lambda r: r.Coverage
+            ChainCoverage = lambda r: r.Coverage
+            ContextData = lambda r: (r.ClassDef,)
+            ChainContextData = lambda r: (
+                r.BacktrackClassDef,
+                r.InputClassDef,
+                r.LookAheadClassDef,
+            )
+
+            def SetContextData(r, d):
+                (r.ClassDef,) = d
+
+            def SetChainContextData(r, d):
+                r.BacktrackClassDef, r.InputClassDef, r.LookAheadClassDef = d
+
+            RuleData = lambda r: (r.Class,)
+            ChainRuleData = lambda r: (r.Backtrack, r.Input, r.LookAhead)
+
+            def SetRuleData(r, d):
+                (r.Class,) = d
+                (r.GlyphCount,) = (len(x) + 1 for x in d)
+
+            def ChainSetRuleData(r, d):
+                r.Backtrack, r.Input, r.LookAhead = d
+                (
+                    r.BacktrackGlyphCount,
+                    r.InputGlyphCount,
+                    r.LookAheadGlyphCount,
+                ) = (len(d[0]), len(d[1]) + 1, len(d[2]))
+
+        elif Format == 3:
+            Coverage = lambda r: r.Coverage[0]
+            ChainCoverage = lambda r: r.InputCoverage[0]
+            ContextData = None
+            ChainContextData = None
+            SetContextData = None
+            SetChainContextData = None
+            RuleData = lambda r: r.Coverage
+            ChainRuleData = lambda r: (
+                r.BacktrackCoverage + r.InputCoverage + r.LookAheadCoverage
+            )
+
+            def SetRuleData(r, d):
+                (r.Coverage,) = d
+                (r.GlyphCount,) = (len(x) for x in d)
+
+            def ChainSetRuleData(r, d):
+                r.BacktrackCoverage, r.InputCoverage, r.LookAheadCoverage = d
+                (
+                    r.BacktrackGlyphCount,
+                    r.InputGlyphCount,
+                    r.LookAheadGlyphCount,
+                ) = (len(x) for x in d)
+
+        else:
+            assert 0, "unknown format: %s" % Format
+
+        if Chain:
+            self.Coverage = ChainCoverage
+            self.ContextData = ChainContextData
+            self.SetContextData = SetChainContextData
+            self.RuleData = ChainRuleData
+            self.SetRuleData = ChainSetRuleData
+        else:
+            self.Coverage = Coverage
+            self.ContextData = ContextData
+            self.SetContextData = SetContextData
+            self.RuleData = RuleData
+            self.SetRuleData = SetRuleData
+
+        if Format == 1:
+            self.Rule = ChainTyp + "Rule"
+            self.RuleCount = ChainTyp + "RuleCount"
+            self.RuleSet = ChainTyp + "RuleSet"
+            self.RuleSetCount = ChainTyp + "RuleSetCount"
+            self.Intersect = lambda glyphs, c, r: [r] if r in glyphs else []
+        elif Format == 2:
+            self.Rule = ChainTyp + "ClassRule"
+            self.RuleCount = ChainTyp + "ClassRuleCount"
+            self.RuleSet = ChainTyp + "ClassSet"
+            self.RuleSetCount = ChainTyp + "ClassSetCount"
+            self.Intersect = lambda glyphs, c, r: (
+                c.intersect_class(glyphs, r)
+                if c
+                else (set(glyphs) if r == 0 else set())
+            )
+
+            self.ClassDef = "InputClassDef" if Chain else "ClassDef"
+            self.ClassDefIndex = 1 if Chain else 0
+            self.Input = "Input" if Chain else "Class"
+
+
+def parseLookupRecords(items, klassName, lookupMap=None):
+    klass = getattr(ot, klassName)
+    lst = []
+    for item in items:
+        rec = klass()
+        item = stripSplitComma(item)
+        assert len(item) == 2, item
+        idx = int(item[0])
+        assert idx > 0, idx
+        rec.SequenceIndex = idx - 1
+        setReference(mapLookup, lookupMap, item[1], setattr, rec, "LookupListIndex")
+        lst.append(rec)
+    return lst
+
+
+def makeClassDef(classDefs, font, klass=ot.Coverage):
+    if not classDefs:
+        return None
+    self = klass()
+    self.classDefs = dict(classDefs)
+    return self
+
+
+def parseClassDef(lines, font, klass=ot.ClassDef):
+    classDefs = {}
+    with lines.between("class definition"):
+        for line in lines:
+            glyph = makeGlyph(line[0])
+            assert glyph not in classDefs, glyph
+            classDefs[glyph] = int(line[1])
+    return makeClassDef(classDefs, font, klass)
+
+
+def makeCoverage(glyphs, font, klass=ot.Coverage):
+    if not glyphs:
+        return None
+    if isinstance(glyphs, set):
+        glyphs = sorted(glyphs)
+    coverage = klass()
+    coverage.glyphs = sorted(set(glyphs), key=font.getGlyphID)
+    return coverage
+
+
+def parseCoverage(lines, font, klass=ot.Coverage):
+    glyphs = []
+    with lines.between("coverage definition"):
+        for line in lines:
+            glyphs.append(makeGlyph(line[0]))
+    return makeCoverage(glyphs, font, klass)
+
+
+def bucketizeRules(self, c, rules, bucketKeys):
+    buckets = {}
+    for seq, recs in rules:
+        buckets.setdefault(seq[c.InputIdx][0], []).append(
+            (tuple(s[1 if i == c.InputIdx else 0 :] for i, s in enumerate(seq)), recs)
+        )
+
+    rulesets = []
+    for firstGlyph in bucketKeys:
+        if firstGlyph not in buckets:
+            rulesets.append(None)
+            continue
+        thisRules = []
+        for seq, recs in buckets[firstGlyph]:
+            rule = getattr(ot, c.Rule)()
+            c.SetRuleData(rule, seq)
+            setattr(rule, c.Type + "Count", len(recs))
+            setattr(rule, c.LookupRecord, recs)
+            thisRules.append(rule)
+
+        ruleset = getattr(ot, c.RuleSet)()
+        setattr(ruleset, c.Rule, thisRules)
+        setattr(ruleset, c.RuleCount, len(thisRules))
+        rulesets.append(ruleset)
+
+    setattr(self, c.RuleSet, rulesets)
+    setattr(self, c.RuleSetCount, len(rulesets))
+
+
+def parseContext(lines, font, Type, lookupMap=None):
+    self = getattr(ot, Type)()
+    typ = lines.peeks()[0].split()[0].lower()
+    if typ == "glyph":
+        self.Format = 1
+        log.debug("Parsing %s format %s", Type, self.Format)
+        c = ContextHelper(Type, self.Format)
+        rules = []
+        for line in lines:
+            assert line[0].lower() == "glyph", line[0]
+            while len(line) < 1 + c.DataLen:
+                line.append("")
+            seq = tuple(makeGlyphs(stripSplitComma(i)) for i in line[1 : 1 + c.DataLen])
+            recs = parseLookupRecords(line[1 + c.DataLen :], c.LookupRecord, lookupMap)
+            rules.append((seq, recs))
+
+        firstGlyphs = set(seq[c.InputIdx][0] for seq, recs in rules)
+        self.Coverage = makeCoverage(firstGlyphs, font)
+        bucketizeRules(self, c, rules, self.Coverage.glyphs)
+    elif typ.endswith("class"):
+        self.Format = 2
+        log.debug("Parsing %s format %s", Type, self.Format)
+        c = ContextHelper(Type, self.Format)
+        classDefs = [None] * c.DataLen
+        while lines.peeks()[0].endswith("class definition begin"):
+            typ = lines.peek()[0][: -len("class definition begin")].lower()
+            idx, klass = {
+                1: {
+                    "": (0, ot.ClassDef),
+                },
+                3: {
+                    "backtrack": (0, ot.BacktrackClassDef),
+                    "": (1, ot.InputClassDef),
+                    "lookahead": (2, ot.LookAheadClassDef),
+                },
+            }[c.DataLen][typ]
+            assert classDefs[idx] is None, idx
+            classDefs[idx] = parseClassDef(lines, font, klass=klass)
+        c.SetContextData(self, classDefs)
+        rules = []
+        for line in lines:
+            assert line[0].lower().startswith("class"), line[0]
+            while len(line) < 1 + c.DataLen:
+                line.append("")
+            seq = tuple(intSplitComma(i) for i in line[1 : 1 + c.DataLen])
+            recs = parseLookupRecords(line[1 + c.DataLen :], c.LookupRecord, lookupMap)
+            rules.append((seq, recs))
+        firstClasses = set(seq[c.InputIdx][0] for seq, recs in rules)
+        firstGlyphs = set(
+            g for g, c in classDefs[c.InputIdx].classDefs.items() if c in firstClasses
+        )
+        self.Coverage = makeCoverage(firstGlyphs, font)
+        bucketizeRules(self, c, rules, range(max(firstClasses) + 1))
+    elif typ.endswith("coverage"):
+        self.Format = 3
+        log.debug("Parsing %s format %s", Type, self.Format)
+        c = ContextHelper(Type, self.Format)
+        coverages = tuple([] for i in range(c.DataLen))
+        while lines.peeks()[0].endswith("coverage definition begin"):
+            typ = lines.peek()[0][: -len("coverage definition begin")].lower()
+            idx, klass = {
+                1: {
+                    "": (0, ot.Coverage),
+                },
+                3: {
+                    "backtrack": (0, ot.BacktrackCoverage),
+                    "input": (1, ot.InputCoverage),
+                    "lookahead": (2, ot.LookAheadCoverage),
+                },
+            }[c.DataLen][typ]
+            coverages[idx].append(parseCoverage(lines, font, klass=klass))
+        c.SetRuleData(self, coverages)
+        lines = list(lines)
+        assert len(lines) == 1
+        line = lines[0]
+        assert line[0].lower() == "coverage", line[0]
+        recs = parseLookupRecords(line[1:], c.LookupRecord, lookupMap)
+        setattr(self, c.Type + "Count", len(recs))
+        setattr(self, c.LookupRecord, recs)
+    else:
+        assert 0, typ
+    return self
+
+
+def parseContextSubst(lines, font, lookupMap=None):
+    return parseContext(lines, font, "ContextSubst", lookupMap=lookupMap)
+
+
+def parseContextPos(lines, font, lookupMap=None):
+    return parseContext(lines, font, "ContextPos", lookupMap=lookupMap)
+
+
+def parseChainedSubst(lines, font, lookupMap=None):
+    return parseContext(lines, font, "ChainContextSubst", lookupMap=lookupMap)
+
+
+def parseChainedPos(lines, font, lookupMap=None):
+    return parseContext(lines, font, "ChainContextPos", lookupMap=lookupMap)
+
+
+def parseReverseChainedSubst(lines, font, _lookupMap=None):
+    self = ot.ReverseChainSingleSubst()
+    self.Format = 1
+    coverages = ([], [])
+    while lines.peeks()[0].endswith("coverage definition begin"):
+        typ = lines.peek()[0][: -len("coverage definition begin")].lower()
+        idx, klass = {
+            "backtrack": (0, ot.BacktrackCoverage),
+            "lookahead": (1, ot.LookAheadCoverage),
+        }[typ]
+        coverages[idx].append(parseCoverage(lines, font, klass=klass))
+    self.BacktrackCoverage = coverages[0]
+    self.BacktrackGlyphCount = len(self.BacktrackCoverage)
+    self.LookAheadCoverage = coverages[1]
+    self.LookAheadGlyphCount = len(self.LookAheadCoverage)
+    mapping = {}
+    for line in lines:
+        assert len(line) == 2, line
+        line = makeGlyphs(line)
+        mapping[line[0]] = line[1]
+    self.Coverage = makeCoverage(set(mapping.keys()), font)
+    self.Substitute = [mapping[k] for k in self.Coverage.glyphs]
+    self.GlyphCount = len(self.Substitute)
+    return self
+
+
+def parseLookup(lines, tableTag, font, lookupMap=None):
+    line = lines.expect("lookup")
+    _, name, typ = line
+    log.debug("Parsing lookup type %s %s", typ, name)
+    lookup = ot.Lookup()
+    lookup.LookupFlag, filterset = parseLookupFlags(lines)
+    if filterset is not None:
+        lookup.MarkFilteringSet = filterset
+    lookup.LookupType, parseLookupSubTable = {
+        "GSUB": {
+            "single": (1, parseSingleSubst),
+            "multiple": (2, parseMultiple),
+            "alternate": (3, parseAlternate),
+            "ligature": (4, parseLigature),
+            "context": (5, parseContextSubst),
+            "chained": (6, parseChainedSubst),
+            "reversechained": (8, parseReverseChainedSubst),
+        },
+        "GPOS": {
+            "single": (1, parseSinglePos),
+            "pair": (2, parsePair),
+            "kernset": (2, parseKernset),
+            "cursive": (3, parseCursive),
+            "mark to base": (4, parseMarkToBase),
+            "mark to ligature": (5, parseMarkToLigature),
+            "mark to mark": (6, parseMarkToMark),
+            "context": (7, parseContextPos),
+            "chained": (8, parseChainedPos),
+        },
+    }[tableTag][typ]
+
+    with lines.until("lookup end"):
+        subtables = []
+
+        while lines.peek():
+            with lines.until(("% subtable", "subtable end")):
+                while lines.peek():
+                    subtable = parseLookupSubTable(lines, font, lookupMap)
+                    assert lookup.LookupType == subtable.LookupType
+                    subtables.append(subtable)
+            if lines.peeks()[0] in ("% subtable", "subtable end"):
+                next(lines)
+    lines.expect("lookup end")
+
+    lookup.SubTable = subtables
+    lookup.SubTableCount = len(lookup.SubTable)
+    if lookup.SubTableCount == 0:
+        # Remove this return when following is fixed:
+        # https://github.com/fonttools/fonttools/issues/789
+        return None
+    return lookup
+
+
+def parseGSUBGPOS(lines, font, tableTag):
+    container = ttLib.getTableClass(tableTag)()
+    lookupMap = DeferredMapping()
+    featureMap = DeferredMapping()
+    assert tableTag in ("GSUB", "GPOS")
+    log.debug("Parsing %s", tableTag)
+    self = getattr(ot, tableTag)()
+    self.Version = 0x00010000
+    fields = {
+        "script table begin": (
+            "ScriptList",
+            lambda lines: parseScriptList(lines, featureMap),
+        ),
+        "feature table begin": (
+            "FeatureList",
+            lambda lines: parseFeatureList(lines, lookupMap, featureMap),
+        ),
+        "lookup": ("LookupList", None),
+    }
+    for attr, parser in fields.values():
+        setattr(self, attr, None)
+    while lines.peek() is not None:
+        typ = lines.peek()[0].lower()
+        if typ not in fields:
+            log.debug("Skipping %s", lines.peek())
+            next(lines)
+            continue
+        attr, parser = fields[typ]
+        if typ == "lookup":
+            if self.LookupList is None:
+                self.LookupList = ot.LookupList()
+                self.LookupList.Lookup = []
+            _, name, _ = lines.peek()
+            lookup = parseLookup(lines, tableTag, font, lookupMap)
+            if lookupMap is not None:
+                assert name not in lookupMap, "Duplicate lookup name: %s" % name
+                lookupMap[name] = len(self.LookupList.Lookup)
+            else:
+                assert int(name) == len(self.LookupList.Lookup), "%d %d" % (
+                    name,
+                    len(self.Lookup),
+                )
+            self.LookupList.Lookup.append(lookup)
+        else:
+            assert getattr(self, attr) is None, attr
+            setattr(self, attr, parser(lines))
+    if self.LookupList:
+        self.LookupList.LookupCount = len(self.LookupList.Lookup)
+    if lookupMap is not None:
+        lookupMap.applyDeferredMappings()
+        if os.environ.get(LOOKUP_DEBUG_ENV_VAR):
+            if "Debg" not in font:
+                font["Debg"] = newTable("Debg")
+                font["Debg"].data = {}
+            debug = (
+                font["Debg"]
+                .data.setdefault(LOOKUP_DEBUG_INFO_KEY, {})
+                .setdefault(tableTag, {})
+            )
+            for name, lookup in lookupMap.items():
+                debug[str(lookup)] = ["", name, ""]
+
+        featureMap.applyDeferredMappings()
+    container.table = self
+    return container
+
+
+def parseGSUB(lines, font):
+    return parseGSUBGPOS(lines, font, "GSUB")
+
+
+def parseGPOS(lines, font):
+    return parseGSUBGPOS(lines, font, "GPOS")
+
+
+def parseAttachList(lines, font):
+    points = {}
+    with lines.between("attachment list"):
+        for line in lines:
+            glyph = makeGlyph(line[0])
+            assert glyph not in points, glyph
+            points[glyph] = [int(i) for i in line[1:]]
+    return otl.buildAttachList(points, font.getReverseGlyphMap())
+
+
+def parseCaretList(lines, font):
+    carets = {}
+    with lines.between("carets"):
+        for line in lines:
+            glyph = makeGlyph(line[0])
+            assert glyph not in carets, glyph
+            num = int(line[1])
+            thisCarets = [int(i) for i in line[2:]]
+            assert num == len(thisCarets), line
+            carets[glyph] = thisCarets
+    return otl.buildLigCaretList(carets, {}, font.getReverseGlyphMap())
+
+
+def makeMarkFilteringSets(sets, font):
+    self = ot.MarkGlyphSetsDef()
+    self.MarkSetTableFormat = 1
+    self.MarkSetCount = 1 + max(sets.keys())
+    self.Coverage = [None] * self.MarkSetCount
+    for k, v in sorted(sets.items()):
+        self.Coverage[k] = makeCoverage(set(v), font)
+    return self
+
+
+def parseMarkFilteringSets(lines, font):
+    sets = {}
+    with lines.between("set definition"):
+        for line in lines:
+            assert len(line) == 2, line
+            glyph = makeGlyph(line[0])
+            # TODO accept set names
+            st = int(line[1])
+            if st not in sets:
+                sets[st] = []
+            sets[st].append(glyph)
+    return makeMarkFilteringSets(sets, font)
+
+
+def parseGDEF(lines, font):
+    container = ttLib.getTableClass("GDEF")()
+    log.debug("Parsing GDEF")
+    self = ot.GDEF()
+    fields = {
+        "class definition begin": (
+            "GlyphClassDef",
+            lambda lines, font: parseClassDef(lines, font, klass=ot.GlyphClassDef),
+        ),
+        "attachment list begin": ("AttachList", parseAttachList),
+        "carets begin": ("LigCaretList", parseCaretList),
+        "mark attachment class definition begin": (
+            "MarkAttachClassDef",
+            lambda lines, font: parseClassDef(lines, font, klass=ot.MarkAttachClassDef),
+        ),
+        "markfilter set definition begin": ("MarkGlyphSetsDef", parseMarkFilteringSets),
+    }
+    for attr, parser in fields.values():
+        setattr(self, attr, None)
+    while lines.peek() is not None:
+        typ = lines.peek()[0].lower()
+        if typ not in fields:
+            log.debug("Skipping %s", typ)
+            next(lines)
+            continue
+        attr, parser = fields[typ]
+        assert getattr(self, attr) is None, attr
+        setattr(self, attr, parser(lines, font))
+    self.Version = 0x00010000 if self.MarkGlyphSetsDef is None else 0x00010002
+    container.table = self
+    return container
+
+
+def parseCmap(lines, font):
+    container = ttLib.getTableClass("cmap")()
+    log.debug("Parsing cmap")
+    tables = []
+    while lines.peek() is not None:
+        lines.expect("cmap subtable %d" % len(tables))
+        platId, encId, fmt, lang = [
+            parseCmapId(lines, field)
+            for field in ("platformID", "encodingID", "format", "language")
+        ]
+        table = cmap_classes[fmt](fmt)
+        table.platformID = platId
+        table.platEncID = encId
+        table.language = lang
+        table.cmap = {}
+        line = next(lines)
+        while line[0] != "end subtable":
+            table.cmap[int(line[0], 16)] = line[1]
+            line = next(lines)
+        tables.append(table)
+    container.tableVersion = 0
+    container.tables = tables
+    return container
+
+
+def parseCmapId(lines, field):
+    line = next(lines)
+    assert field == line[0]
+    return int(line[1])
+
+
+def parseTable(lines, font, tableTag=None):
+    log.debug("Parsing table")
+    line = lines.peeks()
+    tag = None
+    if line[0].split()[0] == "FontDame":
+        tag = line[0].split()[1]
+    elif " ".join(line[0].split()[:3]) == "Font Chef Table":
+        tag = line[0].split()[3]
+    if tag is not None:
+        next(lines)
+        tag = tag.ljust(4)
+        if tableTag is None:
+            tableTag = tag
+        else:
+            assert tableTag == tag, (tableTag, tag)
+
+    assert (
+        tableTag is not None
+    ), "Don't know what table to parse and data doesn't specify"
+
+    return {
+        "GSUB": parseGSUB,
+        "GPOS": parseGPOS,
+        "GDEF": parseGDEF,
+        "cmap": parseCmap,
+    }[tableTag](lines, font)
+
+
+class Tokenizer(object):
+    def __init__(self, f):
+        # TODO BytesIO / StringIO as needed?  also, figure out whether we work on bytes or unicode
+        lines = iter(f)
+        try:
+            self.filename = f.name
+        except:
+            self.filename = None
+        self.lines = iter(lines)
+        self.line = ""
+        self.lineno = 0
+        self.stoppers = []
+        self.buffer = None
+
+    def __iter__(self):
+        return self
+
+    def _next_line(self):
+        self.lineno += 1
+        line = self.line = next(self.lines)
+        line = [s.strip() for s in line.split("\t")]
+        if len(line) == 1 and not line[0]:
+            del line[0]
+        if line and not line[-1]:
+            log.warning("trailing tab found on line %d: %s" % (self.lineno, self.line))
+            while line and not line[-1]:
+                del line[-1]
+        return line
+
+    def _next_nonempty(self):
+        while True:
+            line = self._next_line()
+            # Skip comments and empty lines
+            if line and line[0] and (line[0][0] != "%" or line[0] == "% subtable"):
+                return line
+
+    def _next_buffered(self):
+        if self.buffer:
+            ret = self.buffer
+            self.buffer = None
+            return ret
+        else:
+            return self._next_nonempty()
+
+    def __next__(self):
+        line = self._next_buffered()
+        if line[0].lower() in self.stoppers:
+            self.buffer = line
+            raise StopIteration
+        return line
+
+    def next(self):
+        return self.__next__()
+
+    def peek(self):
+        if not self.buffer:
+            try:
+                self.buffer = self._next_nonempty()
+            except StopIteration:
+                return None
+        if self.buffer[0].lower() in self.stoppers:
+            return None
+        return self.buffer
+
+    def peeks(self):
+        ret = self.peek()
+        return ret if ret is not None else ("",)
+
+    @contextmanager
+    def between(self, tag):
+        start = tag + " begin"
+        end = tag + " end"
+        self.expectendswith(start)
+        self.stoppers.append(end)
+        yield
+        del self.stoppers[-1]
+        self.expect(tag + " end")
+
+    @contextmanager
+    def until(self, tags):
+        if type(tags) is not tuple:
+            tags = (tags,)
+        self.stoppers.extend(tags)
+        yield
+        del self.stoppers[-len(tags) :]
+
+    def expect(self, s):
+        line = next(self)
+        tag = line[0].lower()
+        assert tag == s, "Expected '%s', got '%s'" % (s, tag)
+        return line
+
+    def expectendswith(self, s):
+        line = next(self)
+        tag = line[0].lower()
+        assert tag.endswith(s), "Expected '*%s', got '%s'" % (s, tag)
+        return line
+
+
+def build(f, font, tableTag=None):
+    """Convert a Monotype font layout file to an OpenType layout object
+
+    A font object must be passed, but this may be a "dummy" font; it is only
+    used for sorting glyph sets when making coverage tables and to hold the
+    OpenType layout table while it is being built.
+
+    Args:
+            f: A file object.
+            font (TTFont): A font object.
+            tableTag (string): If provided, asserts that the file contains data for the
+                    given OpenType table.
+
+    Returns:
+            An object representing the table. (e.g. ``table_G_S_U_B_``)
+    """
+    lines = Tokenizer(f)
+    return parseTable(lines, font, tableTag=tableTag)
+
+
+def main(args=None, font=None):
+    """Convert a FontDame OTL file to TTX XML
+
+    Writes XML output to stdout.
+
+    Args:
+            args: Command line arguments (``--font``, ``--table``, input files).
+    """
+    import sys
+    from fontTools import configLogger
+    from fontTools.misc.testTools import MockFont
+
+    if args is None:
+        args = sys.argv[1:]
+
+    # configure the library logger (for >= WARNING)
+    configLogger()
+    # comment this out to enable debug messages from mtiLib's logger
+    # log.setLevel(logging.DEBUG)
+
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        "fonttools mtiLib",
+        description=main.__doc__,
+    )
+
+    parser.add_argument(
+        "--font",
+        "-f",
+        metavar="FILE",
+        dest="font",
+        help="Input TTF files (used for glyph classes and sorting coverage tables)",
+    )
+    parser.add_argument(
+        "--table",
+        "-t",
+        metavar="TABLE",
+        dest="tableTag",
+        help="Table to fill (sniffed from input file if not provided)",
+    )
+    parser.add_argument(
+        "inputs", metavar="FILE", type=str, nargs="+", help="Input FontDame .txt files"
+    )
+
+    args = parser.parse_args(args)
+
+    if font is None:
+        if args.font:
+            font = ttLib.TTFont(args.font)
+        else:
+            font = MockFont()
+
+    for f in args.inputs:
+        log.debug("Processing %s", f)
+        with open(f, "rt", encoding="utf-8-sig") as f:
+            table = build(f, font, tableTag=args.tableTag)
+        blob = table.compile(font)  # Make sure it compiles
+        decompiled = table.__class__()
+        decompiled.decompile(blob, font)  # Make sure it decompiles!
+
+        # continue
+        from fontTools.misc import xmlWriter
+
+        tag = table.tableTag
+        writer = xmlWriter.XMLWriter(sys.stdout)
+        writer.begintag(tag)
+        writer.newline()
+        # table.toXML(writer, font)
+        decompiled.toXML(writer, font)
+        writer.endtag(tag)
+        writer.newline()
+
+
+if __name__ == "__main__":
+    import sys
+
+    sys.exit(main())

shared/package-files/31/31ea913829659f4a9cf5bcbe83458d7c0c765a518c6d9b6808e5a2b10da79019/blob

@@ -0,0 +1,335 @@
+"""
+Implements Lilliefors corrected Kolmogorov-Smirnov tests for normal and
+exponential distributions.
+
+`kstest_fit` is provided as a top-level function to access both tests.
+`kstest_normal` and `kstest_exponential` are provided as convenience functions
+with the appropriate test as the default.
+`lilliefors` is provided as an alias for `kstest_fit`.
+
+Created on Sat Oct 01 13:16:49 2011
+
+Author: Josef Perktold
+License: BSD-3
+
+pvalues for Lilliefors test are based on formula and table in
+
+An Analytic Approximation to the Distribution of Lilliefors's Test Statistic
+for Normality
+Author(s): Gerard E. Dallal and Leland Wilkinson
+Source: The American Statistician, Vol. 40, No. 4 (Nov., 1986), pp. 294-296
+Published by: American Statistical Association
+Stable URL: http://www.jstor.org/stable/2684607.
+
+On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown
+Hubert W. Lilliefors
+Journal of the American Statistical Association, Vol. 62, No. 318.
+(Jun., 1967), pp. 399-402.
+
+---
+
+Updated 2017-07-23
+Jacob C. Kimmel
+
+Ref:
+Lilliefors, H.W.
+On the Kolmogorov-Smirnov test for the exponential distribution with mean
+unknown. Journal of the American Statistical Association, Vol 64, No. 325.
+(1969), pp. 387-389.
+"""
+
+from functools import cache, partial
+
+import numpy as np
+from scipy import stats
+
+from statsmodels.tools.validation import string_like
+
+from .tabledist import TableDist
+
+
+def _make_asymptotic_function(params):
+    """
+    Generate an asymptotic distribution callable from a param matrix
+
+    Polynomial is a[0] * x**(-1/2) + a[1] * x**(-1) + a[2] * x**(-3/2)
+
+    Parameters
+    ----------
+    params : ndarray
+        Array with shape (nalpha, 3) where nalpha is the number of
+        significance levels.
+
+    Returns
+    -------
+    callable
+        Function of the sample size that evaluates the asymptotic
+        critical value approximation.
+    """
+
+    def f(n):
+        poly = np.array([1, np.log(n), np.log(n) ** 2])
+        return np.exp(poly.dot(params.T))
+
+    return f
+
+
+def ksstat(x, cdf, alternative="two-sided", args=()):
+    """
+    Calculate statistic for the Kolmogorov-Smirnov test for goodness of fit
+
+    This calculates the test statistic for a test of the distribution G(x) of
+    an observed variable against a given distribution F(x). Under the null
+    hypothesis the two distributions are identical, G(x)=F(x). The
+    alternative hypothesis can be either 'two-sided' (default), 'lower'
+    or 'upper'. The KS test is only valid for continuous distributions.
+
+    Parameters
+    ----------
+    x : array_like, 1d
+        Array of observations.
+    cdf : str or callable
+        String: name of a distribution in scipy.stats.
+        Callable: function to evaluate cdf.
+    alternative : {'two-sided', 'lower', 'upper'}, optional
+        Defines the alternative hypothesis (see explanation). Default is
+        'two-sided'.
+    args : tuple, optional
+        Distribution parameters for call to cdf.
+
+    Returns
+    -------
+    D : float
+        KS test statistic, either D, D+ or D-.
+
+    See Also
+    --------
+    scipy.stats.kstest
+
+    Notes
+    -----
+    In the one-sided test, the alternative is that the empirical
+    cumulative distribution function of the random variable is "lower"
+    or "upper" than the cumulative distribution function F(x) of the
+    hypothesis, G(x)<=F(x), resp. G(x)>=F(x).
+
+    In contrast to scipy.stats.kstest, this function only calculates the
+    statistic which can be used either as distance measure or to implement
+    case specific p-values.
+    """
+    alternative = string_like(
+        alternative,
+        "alternative",
+        options=("two-sided", "lower", "upper"),
+        deprecated={"two_sided": "two-sided", "less": "lower", "greater": "upper"},
+        removed_after="0.16",
+    )
+    nobs = float(len(x))
+
+    if isinstance(cdf, str):
+        cdf = getattr(stats.distributions, cdf).cdf
+    elif hasattr(cdf, "cdf"):
+        cdf = cdf.cdf
+
+    x = np.sort(x)
+    cdfvals = cdf(x, *args)
+
+    d_plus = (np.arange(1.0, nobs + 1) / nobs - cdfvals).max()
+    d_min = (cdfvals - np.arange(0.0, nobs) / nobs).max()
+    if alternative == "upper":
+        return d_plus
+    elif alternative == "lower":
+        return d_min
+
+    return np.max([d_plus, d_min])
+
+
+@cache
+def get_lilliefors_table(dist="norm"):
+    """
+    Generates tables for significance levels of Lilliefors test statistics
+
+    Tables for available normal and exponential distribution testing,
+    as specified in Lilliefors references above
+
+    Parameters
+    ----------
+    dist : str, optional
+        Distribution being tested, one of {'norm', 'exp'}.
+
+    Returns
+    -------
+    lf : TableDist
+        Table of critical values.
+    """
+    # function just to keep things together
+    # for this test alpha is sf probability, i.e., right tail probability
+
+    # deferred import: this table is a large, generated module (~380 lines
+    # of literal arrays) that is only needed when a Lilliefors test is
+    # actually run, so import it lazily and cache the resulting TableDist
+    # (via lru_cache on this function) rather than paying the parsing cost
+    # at package import time.
+    from ._lilliefors_critical_values import (
+        PERCENTILES,
+        asymp_critical_values,
+        critical_values,
+    )
+
+    alpha = 1 - np.array(PERCENTILES) / 100.0
+    alpha = alpha[::-1]
+    dist = "normal" if dist == "norm" else dist
+    if dist not in critical_values:
+        raise ValueError("Invalid dist parameter. Must be 'norm' or 'exp'")
+    cv_data = critical_values[dist]
+    acv_data = asymp_critical_values[dist]
+
+    size = np.array(sorted(cv_data), dtype=float)
+    crit_lf = np.array([cv_data[key] for key in sorted(cv_data)])
+    crit_lf = crit_lf[:, ::-1]
+
+    asym_params = np.array([acv_data[key] for key in sorted(acv_data)])
+    asymp_fn = _make_asymptotic_function(asym_params[::-1])
+
+    lf = TableDist(alpha, size, crit_lf, asymptotic=asymp_fn)
+    return lf
+
+
+def pval_lf(d_max, n):
+    """
+    Approximate pvalues for Lilliefors test
+
+    This is only valid for pvalues smaller than 0.1 which is not checked in
+    this function.
+
+    Parameters
+    ----------
+    d_max : array_like
+        two-sided Kolmogorov-Smirnov test statistic
+    n : int or float
+        sample size
+
+    Returns
+    -------
+    p-value : float or ndarray
+        pvalue according to approximation formula of Dallal and Wilkinson.
+
+    Notes
+    -----
+    This is mainly a helper function where the calling code should dispatch
+    on bound violations. Therefore it does not check whether the pvalue is in
+    the valid range.
+
+    Precision for the pvalues is around 2 to 3 decimals. This approximation is
+    also used by other statistical packages (e.g., R:fBasics) but might not be
+    the most precise available.
+
+    References
+    ----------
+    DallalWilkinson1986
+    """
+    # todo: check boundaries, valid range for n and Dmax
+    if n > 100:
+        d_max *= (n / 100.0) ** 0.49
+        n = 100
+    pval = np.exp(
+        -7.01256 * d_max**2 * (n + 2.78019)
+        + 2.99587 * d_max * np.sqrt(n + 2.78019)
+        - 0.122119
+        + 0.974598 / np.sqrt(n)
+        + 1.67997 / n
+    )
+    return pval
+
+
+def kstest_fit(x, dist="norm", pvalmethod="table"):
+    """
+    Test assumed normal or exponential distribution using Lilliefors' test
+
+    Lilliefors' test is a Kolmogorov-Smirnov test with estimated parameters.
+
+    Parameters
+    ----------
+    x : array_like, 1d
+        Data to test.
+    dist : {'norm', 'exp'}, optional
+        The assumed distribution.
+    pvalmethod : {'approx', 'table'}, optional
+        The method used to compute the p-value of the test statistic. In
+        general, 'table' is preferred and makes use of a very large simulation.
+        'approx' is only valid for normality. If `dist = 'exp'`, `table` is
+        always used. 'approx' uses the approximation formula of Dalal and
+        Wilkinson, valid for pvalues < 0.1. If the pvalue is larger than 0.1,
+        then the result of `table` is returned.
+
+    Returns
+    -------
+    ksstat : float
+        Kolmogorov-Smirnov test statistic with estimated mean and variance.
+    pvalue : float
+        If the pvalue is lower than some threshold, e.g., 0.05, then we can
+        reject the Null hypothesis that the sample comes from a normal
+        distribution.
+
+    Notes
+    -----
+    'table' uses an improved table based on 10,000,000 simulations. The
+    critical values are approximated using
+    log(cv_alpha) = b_alpha + c[0] log(n) + c[1] log(n)**2
+    where cv_alpha is the critical value for a test with size alpha,
+    b_alpha is an alpha-specific intercept term and c[1] and c[2] are
+    coefficients that are shared across all alphas.
+    Values in the table are linearly interpolated. Values outside the
+    range are returned as bounds, 0.990 for large and 0.001 for small
+    pvalues.
+
+    For implementation details, see lilliefors_critical_value_simulation.py in
+    the test directory.
+    """
+    dist = string_like(dist, "dist", options=("norm", "exp"))
+    pvalmethod = string_like(pvalmethod, "pvalmethod", options=("approx", "table"))
+    x = np.asarray(x)
+    if x.ndim == 2 and x.shape[1] == 1:
+        x = x[:, 0]
+    elif x.ndim != 1:
+        raise ValueError(
+            "Invalid parameter `x`: must be a one-dimensional"
+            " array-like or a single-column DataFrame"
+        )
+
+    nobs = len(x)
+
+    dist = string_like(dist, "dist", options=("norm", "exp"), lower=False)
+    if dist == "norm":
+        z = (x - x.mean()) / x.std(ddof=1)
+        test_d = stats.norm.cdf
+    else:  # dist == "exp"
+        z = x / x.mean()
+        test_d = stats.expon.cdf
+        pvalmethod = "table"
+    # deferred/cached: only builds (and imports) the critical-value table
+    # for the distribution actually requested
+    lilliefors_table = get_lilliefors_table(dist=dist)
+
+    min_nobs = 4 if dist == "norm" else 3
+    if nobs < min_nobs:
+        raise ValueError(
+            f"Test for distribution {dist} requires at least {min_nobs} observations"
+        )
+
+    d_ks = ksstat(z, test_d, alternative="two-sided")
+
+    if pvalmethod == "approx":
+        pval = pval_lf(d_ks, nobs)
+        # check pval is in desired range
+        if pval > 0.1:
+            pval = lilliefors_table.prob(d_ks, nobs)
+    else:  # pvalmethod == 'table'
+        pval = lilliefors_table.prob(d_ks, nobs)
+
+    return d_ks, pval
+
+
+lilliefors = kstest_fit
+kstest_normal = kstest_fit
+kstest_exponential = partial(kstest_fit, dist="exp")

shared/package-files/31/31eb31c6083704bc6178b671f9d49fdf46fa80aa4e81d557c3a5befae1d2b334/blob

@@ -0,0 +1,88 @@
+import os
+import sys
+from typing import Optional, Tuple
+
+
+def glibc_version_string() -> Optional[str]:
+    "Returns glibc version string, or None if not using glibc."
+    return glibc_version_string_confstr() or glibc_version_string_ctypes()
+
+
+def glibc_version_string_confstr() -> Optional[str]:
+    "Primary implementation of glibc_version_string using os.confstr."
+    # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
+    # to be broken or missing. This strategy is used in the standard library
+    # platform module:
+    # https://github.com/python/cpython/blob/fcf1d003bf4f0100c9d0921ff3d70e1127ca1b71/Lib/platform.py#L175-L183
+    if sys.platform == "win32":
+        return None
+    try:
+        gnu_libc_version = os.confstr("CS_GNU_LIBC_VERSION")
+        if gnu_libc_version is None:
+            return None
+        # os.confstr("CS_GNU_LIBC_VERSION") returns a string like "glibc 2.17":
+        _, version = gnu_libc_version.split()
+    except (AttributeError, OSError, ValueError):
+        # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
+        return None
+    return version
+
+
+def glibc_version_string_ctypes() -> Optional[str]:
+    "Fallback implementation of glibc_version_string using ctypes."
+
+    try:
+        import ctypes
+    except ImportError:
+        return None
+
+    # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
+    # manpage says, "If filename is NULL, then the returned handle is for the
+    # main program". This way we can let the linker do the work to figure out
+    # which libc our process is actually using.
+    process_namespace = ctypes.CDLL(None)
+    try:
+        gnu_get_libc_version = process_namespace.gnu_get_libc_version
+    except AttributeError:
+        # Symbol doesn't exist -> therefore, we are not linked to
+        # glibc.
+        return None
+
+    # Call gnu_get_libc_version, which returns a string like "2.5"
+    gnu_get_libc_version.restype = ctypes.c_char_p
+    version_str = gnu_get_libc_version()
+    # py2 / py3 compatibility:
+    if not isinstance(version_str, str):
+        version_str = version_str.decode("ascii")
+
+    return version_str
+
+
+# platform.libc_ver regularly returns completely nonsensical glibc
+# versions. E.g. on my computer, platform says:
+#
+#   ~$ python2.7 -c 'import platform; print(platform.libc_ver())'
+#   ('glibc', '2.7')
+#   ~$ python3.5 -c 'import platform; print(platform.libc_ver())'
+#   ('glibc', '2.9')
+#
+# But the truth is:
+#
+#   ~$ ldd --version
+#   ldd (Debian GLIBC 2.22-11) 2.22
+#
+# This is unfortunate, because it means that the linehaul data on libc
+# versions that was generated by pip 8.1.2 and earlier is useless and
+# misleading. Solution: instead of using platform, use our code that actually
+# works.
+def libc_ver() -> Tuple[str, str]:
+    """Try to determine the glibc version
+
+    Returns a tuple of strings (lib, version) which default to empty strings
+    in case the lookup fails.
+    """
+    glibc_version = glibc_version_string()
+    if glibc_version is None:
+        return ("", "")
+    else:
+        return ("glibc", glibc_version)

shared/package-files/31/31f0728acd86ea4539fb2e058e18514d00f90d068fe4e385adcf9c882f4988a1/blob

@@ -0,0 +1,139 @@
+import sys
+from typing import Any, Dict, Protocol, Text, Union
+
+MSG_DISCONNECT: int
+MSG_IGNORE: int
+MSG_UNIMPLEMENTED: int
+MSG_DEBUG: int
+MSG_SERVICE_REQUEST: int
+MSG_SERVICE_ACCEPT: int
+MSG_KEXINIT: int
+MSG_NEWKEYS: int
+MSG_USERAUTH_REQUEST: int
+MSG_USERAUTH_FAILURE: int
+MSG_USERAUTH_SUCCESS: int
+MSG_USERAUTH_BANNER: int
+MSG_USERAUTH_PK_OK: int
+MSG_USERAUTH_INFO_REQUEST: int
+MSG_USERAUTH_INFO_RESPONSE: int
+MSG_USERAUTH_GSSAPI_RESPONSE: int
+MSG_USERAUTH_GSSAPI_TOKEN: int
+MSG_USERAUTH_GSSAPI_EXCHANGE_COMPLETE: int
+MSG_USERAUTH_GSSAPI_ERROR: int
+MSG_USERAUTH_GSSAPI_ERRTOK: int
+MSG_USERAUTH_GSSAPI_MIC: int
+HIGHEST_USERAUTH_MESSAGE_ID: int
+MSG_GLOBAL_REQUEST: int
+MSG_REQUEST_SUCCESS: int
+MSG_REQUEST_FAILURE: int
+MSG_CHANNEL_OPEN: int
+MSG_CHANNEL_OPEN_SUCCESS: int
+MSG_CHANNEL_OPEN_FAILURE: int
+MSG_CHANNEL_WINDOW_ADJUST: int
+MSG_CHANNEL_DATA: int
+MSG_CHANNEL_EXTENDED_DATA: int
+MSG_CHANNEL_EOF: int
+MSG_CHANNEL_CLOSE: int
+MSG_CHANNEL_REQUEST: int
+MSG_CHANNEL_SUCCESS: int
+MSG_CHANNEL_FAILURE: int
+
+cMSG_DISCONNECT: bytes
+cMSG_IGNORE: bytes
+cMSG_UNIMPLEMENTED: bytes
+cMSG_DEBUG: bytes
+cMSG_SERVICE_REQUEST: bytes
+cMSG_SERVICE_ACCEPT: bytes
+cMSG_KEXINIT: bytes
+cMSG_NEWKEYS: bytes
+cMSG_USERAUTH_REQUEST: bytes
+cMSG_USERAUTH_FAILURE: bytes
+cMSG_USERAUTH_SUCCESS: bytes
+cMSG_USERAUTH_BANNER: bytes
+cMSG_USERAUTH_PK_OK: bytes
+cMSG_USERAUTH_INFO_REQUEST: bytes
+cMSG_USERAUTH_INFO_RESPONSE: bytes
+cMSG_USERAUTH_GSSAPI_RESPONSE: bytes
+cMSG_USERAUTH_GSSAPI_TOKEN: bytes
+cMSG_USERAUTH_GSSAPI_EXCHANGE_COMPLETE: bytes
+cMSG_USERAUTH_GSSAPI_ERROR: bytes
+cMSG_USERAUTH_GSSAPI_ERRTOK: bytes
+cMSG_USERAUTH_GSSAPI_MIC: bytes
+cMSG_GLOBAL_REQUEST: bytes
+cMSG_REQUEST_SUCCESS: bytes
+cMSG_REQUEST_FAILURE: bytes
+cMSG_CHANNEL_OPEN: bytes
+cMSG_CHANNEL_OPEN_SUCCESS: bytes
+cMSG_CHANNEL_OPEN_FAILURE: bytes
+cMSG_CHANNEL_WINDOW_ADJUST: bytes
+cMSG_CHANNEL_DATA: bytes
+cMSG_CHANNEL_EXTENDED_DATA: bytes
+cMSG_CHANNEL_EOF: bytes
+cMSG_CHANNEL_CLOSE: bytes
+cMSG_CHANNEL_REQUEST: bytes
+cMSG_CHANNEL_SUCCESS: bytes
+cMSG_CHANNEL_FAILURE: bytes
+
+MSG_NAMES: Dict[int, str]
+
+AUTH_SUCCESSFUL: int
+AUTH_PARTIALLY_SUCCESSFUL: int
+AUTH_FAILED: int
+
+OPEN_SUCCEEDED: int
+OPEN_FAILED_ADMINISTRATIVELY_PROHIBITED: int
+OPEN_FAILED_CONNECT_FAILED: int
+OPEN_FAILED_UNKNOWN_CHANNEL_TYPE: int
+OPEN_FAILED_RESOURCE_SHORTAGE: int
+
+CONNECTION_FAILED_CODE: Dict[int, str]
+
+DISCONNECT_SERVICE_NOT_AVAILABLE: int
+DISCONNECT_AUTH_CANCELLED_BY_USER: int
+DISCONNECT_NO_MORE_AUTH_METHODS_AVAILABLE: int
+
+zero_byte: bytes
+one_byte: bytes
+four_byte: bytes
+max_byte: bytes
+cr_byte: bytes
+linefeed_byte: bytes
+crlf: bytes
+if sys.version_info < (3, 0):
+    cr_byte_value: bytes
+    linefeed_byte_value: bytes
+else:
+    cr_byte_value: int
+    linefeed_byte_value: int
+
+class _SupportsAsBytes(Protocol):
+    def asbytes(self) -> bytes: ...
+
+_LikeBytes = Union[bytes, Text, _SupportsAsBytes]
+
+def asbytes(s: _LikeBytes) -> bytes: ...
+
+xffffffff: int
+x80000000: int
+o666: int
+o660: int
+o644: int
+o600: int
+o777: int
+o700: int
+o70: int
+
+DEBUG: int
+INFO: int
+WARNING: int
+ERROR: int
+CRITICAL: int
+
+io_sleep: float
+
+DEFAULT_WINDOW_SIZE: int
+DEFAULT_MAX_PACKET_SIZE: int
+
+MIN_WINDOW_SIZE: int
+MIN_PACKET_SIZE: int
+MAX_WINDOW_SIZE: int

shared/package-files/31/31f09bc8966ed57b0224e092c3abef86ce1e88fe0b64f705f9ef61b8f996fb2b/blob

@@ -0,0 +1,101 @@
+import traceback
+import threading
+import multiprocessing
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import (skip_on_cudasim, skip_under_cuda_memcheck,
+                                CUDATestCase)
+import unittest
+
+try:
+    from concurrent.futures import ThreadPoolExecutor
+except ImportError:
+    has_concurrent_futures = False
+else:
+    has_concurrent_futures = True
+
+
+has_mp_get_context = hasattr(multiprocessing, 'get_context')
+
+
+def check_concurrent_compiling():
+    @cuda.jit
+    def foo(x):
+        x[0] += 1
+
+    def use_foo(x):
+        foo[1, 1](x)
+        return x
+
+    arrays = [cuda.to_device(np.arange(10)) for i in range(10)]
+    expected = np.arange(10)
+    expected[0] += 1
+    with ThreadPoolExecutor(max_workers=4) as e:
+        for ary in e.map(use_foo, arrays):
+            np.testing.assert_equal(ary, expected)
+
+
+def spawn_process_entry(q):
+    try:
+        check_concurrent_compiling()
+    # Catch anything that goes wrong in the threads
+    except:  # noqa: E722
+        msg = traceback.format_exc()
+        q.put('\n'.join(['', '=' * 80, msg]))
+    else:
+        q.put(None)
+
+
+@skip_under_cuda_memcheck('Hangs cuda-memcheck')
+@skip_on_cudasim('disabled for cudasim')
+class TestMultiThreadCompiling(CUDATestCase):
+
+    @unittest.skipIf(not has_concurrent_futures, "no concurrent.futures")
+    def test_concurrent_compiling(self):
+        check_concurrent_compiling()
+
+    @unittest.skipIf(not has_mp_get_context, "no multiprocessing.get_context")
+    def test_spawn_concurrent_compilation(self):
+        # force CUDA context init
+        cuda.get_current_device()
+        # use "spawn" to avoid inheriting the CUDA context
+        ctx = multiprocessing.get_context('spawn')
+
+        q = ctx.Queue()
+        p = ctx.Process(target=spawn_process_entry, args=(q,))
+        p.start()
+        try:
+            err = q.get()
+        finally:
+            p.join()
+        if err is not None:
+            raise AssertionError(err)
+        self.assertEqual(p.exitcode, 0, 'test failed in child process')
+
+    def test_invalid_context_error_with_d2h(self):
+        def d2h(arr, out):
+            out[:] = arr.copy_to_host()
+
+        arr = np.arange(1, 4)
+        out = np.zeros_like(arr)
+        darr = cuda.to_device(arr)
+        th = threading.Thread(target=d2h, args=[darr, out])
+        th.start()
+        th.join()
+        np.testing.assert_equal(arr, out)
+
+    def test_invalid_context_error_with_d2d(self):
+        def d2d(dst, src):
+            dst.copy_to_device(src)
+
+        arr = np.arange(100)
+        common = cuda.to_device(arr)
+        darr = cuda.to_device(np.zeros(common.shape, dtype=common.dtype))
+        th = threading.Thread(target=d2d, args=[darr, common])
+        th.start()
+        th.join()
+        np.testing.assert_equal(darr.copy_to_host(), arr)
+
+
+if __name__ == '__main__':
+    unittest.main()

shared/package-files/31/31f16bfef90158d13d4fc249e83459165a1edab89273841343d613b0571729c2/blob

@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API bool is_inference(const at::Tensor & self);
+} // namespace native
+} // namespace at

shared/package-files/31/31f6496511a73ae245863ac284927466508fa6c2b64a9aab69f57b5649c0afc0/blob

@@ -0,0 +1,164 @@
+from __future__ import annotations
+
+from ..._explanation import Explanation
+from .._explainer import Explainer
+
+
+class DeepExplainer(Explainer):
+    """Meant to approximate SHAP values for deep learning models.
+
+    This is an enhanced version of the DeepLIFT algorithm (Deep SHAP) where, similar to Kernel SHAP, we
+    approximate the conditional expectations of SHAP values using a selection of background samples.
+    Lundberg and Lee, NIPS 2017 showed that the per node attribution rules in DeepLIFT (Shrikumar,
+    Greenside, and Kundaje, arXiv 2017) can be chosen to approximate Shapley values. By integrating
+    over many background samples, Deep estimates approximate SHAP values such that they sum
+    up to the difference between the expected model output on the passed background samples and the
+    current model output (f(x) - E[f(x)]).
+
+    Examples
+    --------
+    See :ref:`Deep Explainer Examples <deep_explainer_examples>`
+
+    """
+
+    def __init__(self, model, data, session=None, learning_phase_flags=None):
+        """An explainer object for a differentiable model using a given background dataset.
+
+        Note that the complexity of the method scales linearly with the number of background data
+        samples. Passing the entire training dataset as `data` will give very accurate expected
+        values, but will be unreasonably expensive. The variance of the expectation estimates scales by
+        roughly 1/sqrt(N) for N background data samples. So 100 samples will give a good estimate,
+        and 1000 samples a very good estimate of the expected values.
+
+        Parameters
+        ----------
+        model :
+            if framework == 'tensorflow', (input : [tf.Tensor], output : tf.Tensor)
+            A pair of TensorFlow tensors (or a list and a tensor) that specifies the input and
+            output of the model to be explained. Note that SHAP values are specific to a single
+            output value, so the output tf.Tensor should be a single dimensional output (,1).
+
+            if framework == 'pytorch', an nn.Module object (model), or a tuple (model, layer),
+            where both are nn.Module objects.
+            The model is an nn.Module object which takes as input a tensor (or list of tensors) of
+            shape data, and returns a single dimensional output.
+            If the input is a tuple, the returned shap values will be for the input of the
+            layer argument. layer must be a layer in the model, i.e. model.conv2
+
+        data :
+            if framework == 'tensorflow': [np.array] or [pandas.DataFrame]
+            if framework == 'pytorch': [torch.tensor]
+
+            The background dataset to use for integrating out features. Deep integrates
+            over these samples. The data passed here must match the input tensors given in the
+            first argument. Note that, since these samples are integrated over for each sample, you
+            should only use something like 100 or 1000 random background samples, not the whole
+            training dataset.
+
+        session : None or tensorflow.Session
+            The TensorFlow session that has the model we are explaining. If None is passed then
+            we do our best to find the right session, first looking for a keras session, then
+            falling back to the default TensorFlow session.
+
+        learning_phase_flags : None or list of tensors
+            If you have your own custom learning phase flags, pass them here. When explaining a prediction
+            we need to ensure we are not in training mode, since this changes the behavior of ops like
+            batch norm or dropout. If None is passed then we look for tensors in the graph that look like
+            learning phase flags (this works for Keras models). Note that we assume all the flags should
+            have a value of False during predictions (and hence explanations).
+
+        """
+        # first, we need to find the framework
+        if type(model) is tuple:
+            a, b = model
+            try:
+                a.named_parameters()
+                framework = "pytorch"
+            except Exception:
+                framework = "tensorflow"
+        else:
+            try:
+                model.named_parameters()
+                framework = "pytorch"
+            except Exception:
+                framework = "tensorflow"
+
+        masker = data
+        super().__init__(model, masker)
+
+        self.explainer: TFDeep | PyTorchDeep
+        if framework == "tensorflow":
+            from .deep_tf import TFDeep
+
+            self.explainer = TFDeep(model, data, session, learning_phase_flags)
+        elif framework == "pytorch":
+            from .deep_pytorch import PyTorchDeep
+
+            self.explainer = PyTorchDeep(model, data)
+
+        self.expected_value = self.explainer.expected_value
+        self.explainer.framework = framework  # type: ignore
+
+    def __call__(self, X: list | np.ndarray | pd.DataFrame | torch.tensor) -> Explanation:  # type: ignore  # noqa: F821
+        """Return an explanation object for the model applied to X.
+
+        Parameters
+        ----------
+        X : list,
+            if framework == 'tensorflow': numpy.array, or pandas.DataFrame
+            if framework == 'pytorch': torch.tensor
+            A tensor (or list of tensors) of samples (where X.shape[0] == # samples) on which to
+            explain the model's output.
+
+        Returns
+        -------
+        shap.Explanation:
+        """
+        shap_values = self.shap_values(X)
+        return Explanation(values=shap_values, data=X)
+
+    def shap_values(self, X, ranked_outputs=None, output_rank_order="max", check_additivity=True):
+        """Return approximate SHAP values for the model applied to the data given by X.
+
+        Parameters
+        ----------
+        X : list,
+            if framework == 'tensorflow': np.array, or pandas.DataFrame
+            if framework == 'pytorch': torch.tensor
+            A tensor (or list of tensors) of samples (where X.shape[0] == # samples) on which to
+            explain the model's output.
+
+        ranked_outputs : None or int
+            If ranked_outputs is None then we explain all the outputs in a multi-output model. If
+            ranked_outputs is a positive integer then we only explain that many of the top model
+            outputs (where "top" is determined by output_rank_order). Note that this causes a pair
+            of values to be returned (shap_values, indexes), where shap_values is a list of numpy
+            arrays for each of the output ranks, and indexes is a matrix that indicates for each sample
+            which output indexes were choses as "top".
+
+        output_rank_order : "max", "min", or "max_abs"
+            How to order the model outputs when using ranked_outputs, either by maximum, minimum, or
+            maximum absolute value.
+
+        Returns
+        -------
+        np.array or list
+            Estimated SHAP values, usually of shape ``(# samples x # features)``.
+
+            The shape of the returned array depends on the number of model outputs:
+
+            * one input, one output: matrix of shape ``(#num_samples, *X.shape[1:])``.
+            * one input, multiple outputs: matrix of shape ``(#num_samples, *X.shape[1:], #num_outputs)``
+            * multiple inputs, one or more outputs: list of matrices, with shapes of one of the above.
+
+            If ranked_outputs is ``None`` then this list of tensors matches
+            the number of model outputs. If ranked_outputs is a positive integer a pair is returned
+            (shap_values, indexes), where shap_values is a list of tensors with a length of
+            ranked_outputs, and indexes is a matrix that indicates for each sample which output indexes
+            were chosen as "top".
+
+            .. versionchanged:: 0.45.0
+                Return type for models with multiple outputs and one input changed from list to np.ndarray.
+
+        """
+        return self.explainer.shap_values(X, ranked_outputs, output_rank_order, check_additivity=check_additivity)

shared/package-files/31/31f721de5fe36cae5740d7da2e15e9e8e72fa391d42a118db10cc771f2cf1b0d/blob

@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor __lshift__(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & __ilshift__(at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor __lshift__(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & __ilshift__(at::Tensor & self, const at::Tensor & other);
+
+} // namespace cuda
+} // namespace at

shared/package-files/31/31f7e08f7c79b45c2c2968a1c3691b8529a1bc634481073fa498ba0b79844317/blob

@@ -0,0 +1,407 @@
+"""
+    pygments.lexers.wgsl
+    ~~~~~~~~~~~~~~~~~~~~
+
+    Lexer for the WebGPU Shading Language.
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+from pygments.lexer import RegexLexer, include, bygroups, words, default
+from pygments.token import Comment, Operator, Keyword, Name, \
+    Number, Punctuation, Whitespace
+from pygments import unistring as uni
+
+__all__ = ['WgslLexer']
+
+LF = '\\u000a'
+VT = '\\u000b'
+FF = '\\u000c'
+CR = '\\u000d'
+NextLine = '\\u0085'
+LineSep = '\\u2028'
+ParaSep = '\\u2029'
+LineEndCodePoints = [LF,VT,FF,CR,NextLine,LineSep,ParaSep]
+NotLineEndRE = '[^' + "".join(LineEndCodePoints) + ']'
+LineEndRE = '[' + "".join(LineEndCodePoints) + ']'
+
+# https://www.w3.org/TR/WGSL/#syntax-ident_pattern_token
+ident_pattern_token = '([{}][{}]+)|[{}]'.format(uni.xid_start,uni.xid_continue,uni.xid_start)
+
+
+class WgslLexer(RegexLexer):
+    """
+    Lexer for the WebGPU Shading Language.
+
+    .. versionadded:: 2.15
+    """
+    name = 'WebGPU Shading Language'
+    url = 'https://www.w3.org/TR/WGSL/'
+    aliases = ['wgsl']
+    filenames = ['*.wgsl']
+    mimetypes = ['text/wgsl']
+
+    # https://www.w3.org/TR/WGSL/#var-and-value
+    keyword_decl = (words('var let const override'.split(),suffix=r'\b'), Keyword.Declaration)
+    # https://www.w3.org/TR/WGSL/#keyword-summary
+    keywords = (words("""
+                alias
+                break
+                case
+                const_assert
+                continue
+                continuing
+                default
+                diagnostic
+                discard
+                else
+                enable
+                false
+                fn
+                for
+                if
+                loop
+                requires
+                return
+                struct
+                switch
+                true
+                while
+                """.split(), suffix=r'\b'), Keyword)
+
+    # https://www.w3.org/TR/WGSL/#reserved-words
+    keyword_reserved = (words("""
+                NULL
+                Self
+                abstract
+                active
+                alignas
+                alignof
+                as
+                asm
+                asm_fragment
+                async
+                attribute
+                auto
+                await
+                become
+                binding_array
+                cast
+                catch
+                class
+                co_await
+                co_return
+                co_yield
+                coherent
+                column_major
+                common
+                compile
+                compile_fragment
+                concept
+                const_cast
+                consteval
+                constexpr
+                constinit
+                crate
+                debugger
+                decltype
+                delete
+                demote
+                demote_to_helper
+                do
+                dynamic_cast
+                enum
+                explicit
+                export
+                extends
+                extern
+                external
+                fallthrough
+                filter
+                final
+                finally
+                friend
+                from
+                fxgroup
+                get
+                goto
+                groupshared
+                highp
+                impl
+                implements
+                import
+                inline
+                instanceof
+                interface
+                layout
+                lowp
+                macro
+                macro_rules
+                match
+                mediump
+                meta
+                mod
+                module
+                move
+                mut
+                mutable
+                namespace
+                new
+                nil
+                noexcept
+                noinline
+                nointerpolation
+                noperspective
+                null
+                nullptr
+                of
+                operator
+                package
+                packoffset
+                partition
+                pass
+                patch
+                pixelfragment
+                precise
+                precision
+                premerge
+                priv
+                protected
+                pub
+                public
+                readonly
+                ref
+                regardless
+                register
+                reinterpret_cast
+                require
+                resource
+                restrict
+                self
+                set
+                shared
+                sizeof
+                smooth
+                snorm
+                static
+                static_assert
+                static_cast
+                std
+                subroutine
+                super
+                target
+                template
+                this
+                thread_local
+                throw
+                trait
+                try
+                type
+                typedef
+                typeid
+                typename
+                typeof
+                union
+                unless
+                unorm
+                unsafe
+                unsized
+                use
+                using
+                varying
+                virtual
+                volatile
+                wgsl
+                where
+                with
+                writeonly
+                yield
+                """.split(), suffix=r'\b'), Keyword.Reserved)
+
+    # https://www.w3.org/TR/WGSL/#predeclared-enumerants
+    predeclared_enums = (words("""
+          read write read_write
+          function private workgroup uniform storage
+          perspective linear flat
+          center centroid sample
+          vertex_index instance_index position front_facing frag_depth
+              local_invocation_id local_invocation_index
+              global_invocation_id workgroup_id num_workgroups
+              sample_index sample_mask
+          rgba8unorm
+          rgba8snorm
+          rgba8uint
+          rgba8sint
+          rgba16uint
+          rgba16sint
+          rgba16float
+          r32uint
+          r32sint
+          r32float
+          rg32uint
+          rg32sint
+          rg32float
+          rgba32uint
+          rgba32sint
+          rgba32float
+          bgra8unorm
+          """.split(), suffix=r'\b'), Name.Builtin)
+
+    # https://www.w3.org/TR/WGSL/#predeclared-types
+    predeclared_types = (words("""
+          bool
+          f16
+          f32
+          i32
+          sampler sampler_comparison
+          texture_depth_2d
+          texture_depth_2d_array
+          texture_depth_cube
+          texture_depth_cube_array
+          texture_depth_multisampled_2d
+          texture_external
+          texture_external
+          u32
+          """.split(), suffix=r'\b'), Name.Builtin)
+
+    # https://www.w3.org/TR/WGSL/#predeclared-types
+    predeclared_type_generators = (words("""
+          array
+          atomic
+          mat2x2
+          mat2x3
+          mat2x4
+          mat3x2
+          mat3x3
+          mat3x4
+          mat4x2
+          mat4x3
+          mat4x4
+          ptr
+          texture_1d
+          texture_2d
+          texture_2d_array
+          texture_3d
+          texture_cube
+          texture_cube_array
+          texture_multisampled_2d
+          texture_storage_1d
+          texture_storage_2d
+          texture_storage_2d_array
+          texture_storage_3d
+          vec2
+          vec3
+          vec4
+          """.split(), suffix=r'\b'), Name.Builtin)
+
+    # Predeclared type aliases for vectors
+    # https://www.w3.org/TR/WGSL/#vector-types
+    predeclared_type_alias_vectors = (words("""
+          vec2i vec3i vec4i
+          vec2u vec3u vec4u
+          vec2f vec3f vec4f
+          vec2h vec3h vec4h
+          """.split(), suffix=r'\b'), Name.Builtin)
+
+    # Predeclared type aliases for matrices
+    # https://www.w3.org/TR/WGSL/#matrix-types
+    predeclared_type_alias_matrices = (words("""
+          mat2x2f mat2x3f mat2x4f
+          mat3x2f mat3x3f mat3x4f
+          mat4x2f mat4x3f mat4x4f
+          mat2x2h mat2x3h mat2x4h
+          mat3x2h mat3x3h mat3x4h
+          mat4x2h mat4x3h mat4x4h
+          """.split(), suffix=r'\b'), Name.Builtin)
+
+    tokens = {
+        'blankspace': [
+            # https://www.w3.org/TR/WGSL/#blankspace
+            (r'[\u0020\u0009\u000a\u000b\u000c\u000d\u0085\u200e\u200f\u2028\u2029]+', Whitespace),
+        ],
+        'comments': [
+            # Line ending comments
+            # Match up CR/LF pair first.
+            (r'//{}*{}{}'.format(NotLineEndRE,CR,LF), Comment.Single),
+            (r'//{}*{}'.format(NotLineEndRE,LineEndRE), Comment.Single),
+            (r'/\*', Comment.Multiline, 'block_comment'),
+        ],
+        'attribute': [
+            include('blankspace'),
+            include('comments'),
+            (ident_pattern_token, Name.Decorator,'#pop'),
+            default('#pop'),
+        ],
+        'root': [
+            include('blankspace'),
+            include('comments'),
+
+            # Attributes.
+            # https://www.w3.org/TR/WGSL/#attributes
+            # Mark the '@' and the attribute name as a decorator.
+            (r'@', Name.Decorator, 'attribute'),
+
+            # Keywords
+            (r'(true|false)\b', Keyword.Constant),
+            keyword_decl,
+            keywords,
+            keyword_reserved,
+
+            # Predeclared
+            predeclared_enums,
+            predeclared_types,
+            predeclared_type_generators,
+            predeclared_type_alias_vectors,
+            predeclared_type_alias_matrices,
+
+            # Decimal float literals
+            # https://www.w3.org/TR/WGSL/#syntax-decimal_float_literal
+            # 0, with type-specifying suffix.
+            (r'0[fh]', Number.Float),
+            # Other decimal integer, with type-specifying suffix.
+            (r'[1-9][0-9]*[fh]', Number.Float),
+            #    Has decimal point, at least one digit after decimal.
+            (r'[0-9]*\.[0-9]+([eE][+-]?[0-9]+)?[fh]?', Number.Float),
+            #    Has decimal point, at least one digit before decimal.
+            (r'[0-9]+\.[0-9]*([eE][+-]?[0-9]+)?[fh]?', Number.Float),
+            #    Has at least one digit, and has an exponent.
+            (r'[0-9]+[eE][+-]?[0-9]+[fh]?', Number.Float),
+
+            # Hex float literals
+            # https://www.w3.org/TR/WGSL/#syntax-hex_float_literal
+            (r'0[xX][0-9a-fA-F]*\.[0-9a-fA-F]+([pP][+-]?[0-9]+[fh]?)?', Number.Float),
+            (r'0[xX][0-9a-fA-F]+\.[0-9a-fA-F]*([pP][+-]?[0-9]+[fh]?)?', Number.Float),
+            (r'0[xX][0-9a-fA-F]+[pP][+-]?[0-9]+[fh]?', Number.Float),
+
+            # Hexadecimal integer literals
+            # https://www.w3.org/TR/WGSL/#syntax-hex_int_literal
+            (r'0[xX][0-9a-fA-F]+[iu]?', Number.Hex),
+            # Decimal integer literals
+            # https://www.w3.org/TR/WGSL/#syntax-decimal_int_literal
+            # We need two rules here because 01 is not valid.
+            (r'[1-9][0-9]*[iu]?', Number.Integer),
+            (r'0[iu]?', Number.Integer), # Must match last.
+
+            # Operators and Punctuation
+            (r'[{}()\[\],\.;:]', Punctuation),
+            (r'->', Punctuation), # Return-type arrow
+            (r'[+\-*/%&|<>^!~=]', Operator),
+
+            # TODO: Treat context-depedendent names specially
+            # https://www.w3.org/TR/WGSL/#context-dependent-name
+
+            # Identifiers
+            (ident_pattern_token, Name),
+
+            # TODO: templates start and end tokens.
+            # https://www.w3.org/TR/WGSL/#template-lists-sec
+        ],
+        'block_comment': [
+            # https://www.w3.org/TR/WGSL/#block-comment
+            (r'[^*/]+', Comment.Multiline),
+            (r'/\*', Comment.Multiline, '#push'),
+            (r'\*/', Comment.Multiline, '#pop'),
+            (r'[*/]', Comment.Multiline),
+        ],
+    }

shared/package-files/31/31fe7b070a21467f727ba8b126a8cd1272b480aaa754f13d1b2f2f2beebfcf1a/blob

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+"""
+A module with various ``typing.Protocol`` subclasses that implement
+the ``__call__`` magic method.
+
+See the `Mypy documentation`_ on protocols for more details.
+
+.. _`Mypy documentation`: https://mypy.readthedocs.io/en/stable/protocols.html#callback-protocols
+
+"""
+
+from __future__ import annotations
+
+from typing import (
+    TypeVar,
+    overload,
+    Any,
+    NoReturn,
+    Protocol,
+)
+
+from numpy import (
+    ndarray,
+    dtype,
+    generic,
+    bool_,
+    timedelta64,
+    number,
+    integer,
+    unsignedinteger,
+    signedinteger,
+    int8,
+    int_,
+    floating,
+    float64,
+    complexfloating,
+    complex128,
+)
+from ._nbit import _NBitInt, _NBitDouble
+from ._scalars import (
+    _BoolLike_co,
+    _IntLike_co,
+    _FloatLike_co,
+    _NumberLike_co,
+)
+from . import NBitBase
+from ._array_like import NDArray
+from ._nested_sequence import _NestedSequence
+
+_T1 = TypeVar("_T1")
+_T2 = TypeVar("_T2")
+_T1_contra = TypeVar("_T1_contra", contravariant=True)
+_T2_contra = TypeVar("_T2_contra", contravariant=True)
+_2Tuple = tuple[_T1, _T1]
+
+_NBit1 = TypeVar("_NBit1", bound=NBitBase)
+_NBit2 = TypeVar("_NBit2", bound=NBitBase)
+
+_IntType = TypeVar("_IntType", bound=integer)
+_FloatType = TypeVar("_FloatType", bound=floating)
+_NumberType = TypeVar("_NumberType", bound=number)
+_NumberType_co = TypeVar("_NumberType_co", covariant=True, bound=number)
+_GenericType_co = TypeVar("_GenericType_co", covariant=True, bound=generic)
+
+class _BoolOp(Protocol[_GenericType_co]):
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> int_: ...
+    @overload
+    def __call__(self, other: float, /) -> float64: ...
+    @overload
+    def __call__(self, other: complex, /) -> complex128: ...
+    @overload
+    def __call__(self, other: _NumberType, /) -> _NumberType: ...
+
+class _BoolBitOp(Protocol[_GenericType_co]):
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> int_: ...
+    @overload
+    def __call__(self, other: _IntType, /) -> _IntType: ...
+
+class _BoolSub(Protocol):
+    # Note that `other: bool_` is absent here
+    @overload
+    def __call__(self, other: bool, /) -> NoReturn: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> int_: ...
+    @overload
+    def __call__(self, other: float, /) -> float64: ...
+    @overload
+    def __call__(self, other: complex, /) -> complex128: ...
+    @overload
+    def __call__(self, other: _NumberType, /) -> _NumberType: ...
+
+class _BoolTrueDiv(Protocol):
+    @overload
+    def __call__(self, other: float | _IntLike_co, /) -> float64: ...
+    @overload
+    def __call__(self, other: complex, /) -> complex128: ...
+    @overload
+    def __call__(self, other: _NumberType, /) -> _NumberType: ...
+
+class _BoolMod(Protocol):
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> int8: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> int_: ...
+    @overload
+    def __call__(self, other: float, /) -> float64: ...
+    @overload
+    def __call__(self, other: _IntType, /) -> _IntType: ...
+    @overload
+    def __call__(self, other: _FloatType, /) -> _FloatType: ...
+
+class _BoolDivMod(Protocol):
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> _2Tuple[int8]: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> _2Tuple[int_]: ...
+    @overload
+    def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
+    @overload
+    def __call__(self, other: _IntType, /) -> _2Tuple[_IntType]: ...
+    @overload
+    def __call__(self, other: _FloatType, /) -> _2Tuple[_FloatType]: ...
+
+class _TD64Div(Protocol[_NumberType_co]):
+    @overload
+    def __call__(self, other: timedelta64, /) -> _NumberType_co: ...
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> NoReturn: ...
+    @overload
+    def __call__(self, other: _FloatLike_co, /) -> timedelta64: ...
+
+class _IntTrueDiv(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> floating[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(self, other: integer[_NBit2], /) -> floating[_NBit1 | _NBit2]: ...
+
+class _UnsignedIntOp(Protocol[_NBit1]):
+    # NOTE: `uint64 + signedinteger -> float64`
+    @overload
+    def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
+    @overload
+    def __call__(
+        self, other: int | signedinteger[Any], /
+    ) -> Any: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: unsignedinteger[_NBit2], /
+    ) -> unsignedinteger[_NBit1 | _NBit2]: ...
+
+class _UnsignedIntBitOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> signedinteger[Any]: ...
+    @overload
+    def __call__(self, other: signedinteger[Any], /) -> signedinteger[Any]: ...
+    @overload
+    def __call__(
+        self, other: unsignedinteger[_NBit2], /
+    ) -> unsignedinteger[_NBit1 | _NBit2]: ...
+
+class _UnsignedIntMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
+    @overload
+    def __call__(
+        self, other: int | signedinteger[Any], /
+    ) -> Any: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: unsignedinteger[_NBit2], /
+    ) -> unsignedinteger[_NBit1 | _NBit2]: ...
+
+class _UnsignedIntDivMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ...
+    @overload
+    def __call__(
+        self, other: int | signedinteger[Any], /
+    ) -> _2Tuple[Any]: ...
+    @overload
+    def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
+    @overload
+    def __call__(
+        self, other: unsignedinteger[_NBit2], /
+    ) -> _2Tuple[unsignedinteger[_NBit1 | _NBit2]]: ...
+
+class _SignedIntOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: signedinteger[_NBit2], /,
+    ) -> signedinteger[_NBit1 | _NBit2]: ...
+
+class _SignedIntBitOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(
+        self, other: signedinteger[_NBit2], /,
+    ) -> signedinteger[_NBit1 | _NBit2]: ...
+
+class _SignedIntMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: signedinteger[_NBit2], /,
+    ) -> signedinteger[_NBit1 | _NBit2]: ...
+
+class _SignedIntDivMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ...
+    @overload
+    def __call__(self, other: int, /) -> _2Tuple[signedinteger[_NBit1 | _NBitInt]]: ...
+    @overload
+    def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
+    @overload
+    def __call__(
+        self, other: signedinteger[_NBit2], /,
+    ) -> _2Tuple[signedinteger[_NBit1 | _NBit2]]: ...
+
+class _FloatOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> floating[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: integer[_NBit2] | floating[_NBit2], /
+    ) -> floating[_NBit1 | _NBit2]: ...
+
+class _FloatMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> floating[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: integer[_NBit2] | floating[_NBit2], /
+    ) -> floating[_NBit1 | _NBit2]: ...
+
+class _FloatDivMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> _2Tuple[floating[_NBit1]]: ...
+    @overload
+    def __call__(self, other: int, /) -> _2Tuple[floating[_NBit1 | _NBitInt]]: ...
+    @overload
+    def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
+    @overload
+    def __call__(
+        self, other: integer[_NBit2] | floating[_NBit2], /
+    ) -> _2Tuple[floating[_NBit1 | _NBit2]]: ...
+
+class _ComplexOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> complexfloating[_NBit1, _NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> complexfloating[_NBit1 | _NBitInt, _NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self,
+        other: (
+            integer[_NBit2]
+            | floating[_NBit2]
+            | complexfloating[_NBit2, _NBit2]
+        ), /,
+    ) -> complexfloating[_NBit1 | _NBit2, _NBit1 | _NBit2]: ...
+
+class _NumberOp(Protocol):
+    def __call__(self, other: _NumberLike_co, /) -> Any: ...
+
+class _SupportsLT(Protocol):
+    def __lt__(self, other: Any, /) -> object: ...
+
+class _SupportsGT(Protocol):
+    def __gt__(self, other: Any, /) -> object: ...
+
+class _ComparisonOp(Protocol[_T1_contra, _T2_contra]):
+    @overload
+    def __call__(self, other: _T1_contra, /) -> bool_: ...
+    @overload
+    def __call__(self, other: _T2_contra, /) -> NDArray[bool_]: ...
+    @overload
+    def __call__(
+        self,
+        other: _SupportsLT | _SupportsGT | _NestedSequence[_SupportsLT | _SupportsGT],
+        /,
+    ) -> Any: ...

shared/package-files/32/32027eb7f7fb836922cdcad667f12b725c791e049ac7d269bda12429a39e9d48/blob

@@ -0,0 +1,13 @@
+# See: man pkg-config
+prefix=/opt/conda
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: Python
+Description: Embed Python into an application
+Requires:
+Version: 3.10
+Libs.private: -lcrypt -lpthread -ldl  -lutil -lm
+Libs: -L${libdir} -lpython3.10
+Cflags: -I${includedir}/python3.10

shared/package-files/32/3204305af3d1b1a4fa532c1b68e8ec5d331c02add9f6d6a1772f39ff0ee47c16/blob

@@ -0,0 +1,4 @@
+class ExperimentalWarning(Warning):
+    """Used to manage warning messages for any experimental integrations."""
+
+    pass

shared/package-files/32/3208279bebd7d83b14c1360f25f93cd40048e904280d52b3b8549b05f0e3ae71/blob

@@ -0,0 +1,63 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_fused_sgd_ops.h>
+
+namespace at {
+
+
+// aten::_fused_sgd_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> ()
+inline void _fused_sgd_(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={}) {
+    return at::_ops::_fused_sgd_::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf);
+}
+
+// aten::_fused_sgd_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> ()
+inline void _fused_sgd_(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={}) {
+    return at::_ops::_fused_sgd__tensor_lr::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf);
+}
+
+// aten::_fused_sgd.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
+inline void _fused_sgd_out(at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={}) {
+    return at::_ops::_fused_sgd_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out);
+}
+// aten::_fused_sgd.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
+inline void _fused_sgd_outf(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf, at::TensorList out) {
+    return at::_ops::_fused_sgd_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out);
+}
+
+// aten::_fused_sgd(Tensor[] self, Tensor[] grads, Tensor[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] momentum_buffer_list_out)
+inline ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_sgd(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={}) {
+    return at::_ops::_fused_sgd::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf);
+}
+
+// aten::_fused_sgd.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
+inline void _fused_sgd_out(at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={}) {
+    return at::_ops::_fused_sgd_tensor_lr_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out);
+}
+// aten::_fused_sgd.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
+inline void _fused_sgd_outf(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale, const c10::optional<at::Tensor> & found_inf, at::TensorList out) {
+    return at::_ops::_fused_sgd_tensor_lr_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out);
+}
+
+// aten::_fused_sgd.tensor_lr(Tensor[] self, Tensor[] grads, Tensor[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] momentum_buffer_list_out)
+inline ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_sgd(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional<at::Tensor> & grad_scale={}, const c10::optional<at::Tensor> & found_inf={}) {
+    return at::_ops::_fused_sgd_tensor_lr::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf);
+}
+
+}

shared/package-files/32/320b3d7ae0eb563e60d7a62387de282960b1ab1a94b376935691bb8cc4967ed0/blob

@@ -0,0 +1,1194 @@
+#############################################################################
+# This code draws from the Python Optimal Transport version of the
+# network simplex algorithm, which in turn was adapted from the LEMON
+# library. The copyrights/comment blocks for those are preserved below.
+# The Python/Numba implementation was adapted by Leland McInnes (2020).
+#
+#  * This file has been adapted by Nicolas Bonneel (2013),
+#  * from network_simplex.h from LEMON, a generic C++ optimization library,
+#  * to implement a lightweight network simplex for mass transport, more
+#  * memory efficient that the original file. A previous version of this file
+#  * is used as part of the Displacement Interpolation project,
+#  * Web: http://www.cs.ubc.ca/labs/imager/tr/2011/DisplacementInterpolation/
+#  *
+#  *
+#  **** Original file Copyright Notice :
+#  *
+#  * Copyright (C) 2003-2010
+#  * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport
+#  * (Egervary Research Group on Combinatorial Optimization, EGRES).
+#  *
+#  * Permission to use, modify and distribute this software is granted
+#  * provided that this copyright notice appears in all copies. For
+#  * precise terms see the accompanying LICENSE file.
+#  *
+#  * This software is provided "AS IS" with no warranty of any kind,
+#  * express or implied, and with no claim as to its suitability for any
+#  * purpose.
+
+import numpy as np
+import numba
+from collections import namedtuple
+from enum import Enum, IntEnum
+
+_mock_identity = np.eye(2, dtype=np.float32)
+_mock_ones = np.ones(2, dtype=np.float32)
+_dummy_cost = np.zeros((2, 2), dtype=np.float64)
+
+# Accuracy tolerance and net supply tolerance
+EPSILON = 2.2204460492503131e-15
+NET_SUPPLY_ERROR_TOLERANCE = 1e-8
+
+## Defaults to double for everythig in POT
+INFINITY = np.finfo(np.float64).max
+MAX = np.finfo(np.float64).max
+
+dummy_cost = np.zeros((2, 2), dtype=np.float64)
+
+# Invalid Arc num
+INVALID = -1
+
+# Problem Status
+class ProblemStatus(Enum):
+    OPTIMAL = 0
+    MAX_ITER_REACHED = 1
+    UNBOUNDED = 2
+    INFEASIBLE = 3
+
+
+# Arc States
+class ArcState(IntEnum):
+    STATE_UPPER = -1
+    STATE_TREE = 0
+    STATE_LOWER = 1
+
+
+SpanningTree = namedtuple(
+    "SpanningTree",
+    [
+        "parent",  # int array
+        "pred",  # int array
+        "thread",  # int array
+        "rev_thread",  # int array
+        "succ_num",  # int array
+        "last_succ",  # int array
+        "forward",  # bool array
+        "state",  # state array
+        "root",  # int
+    ],
+)
+DiGraph = namedtuple(
+    "DiGraph",
+    [
+        "n_nodes",  # int
+        "n_arcs",  # int
+        "n",  # int
+        "m",  # int
+        "use_arc_mixing",  # bool
+        "num_total_big_subsequence_numbers",  # int
+        "subsequence_length",  # int
+        "num_big_subsequences",  # int
+        "mixing_coeff",
+    ],
+)
+NodeArcData = namedtuple(
+    "NodeArcData",
+    [
+        "cost",  # double array
+        "supply",  # double array
+        "flow",  # double array
+        "pi",  # double array
+        "source",  # unsigned int array
+        "target",  # unsigned int array
+    ],
+)
+LeavingArcData = namedtuple(
+    "LeavingArcData", ["u_in", "u_out", "v_in", "delta", "change"]
+)
+
+# Just reproduce a simpler version of numpy isclose (not numba supported yet)
+@numba.njit()
+def isclose(a, b, rtol=1.0e-5, atol=EPSILON):
+    diff = np.abs(a - b)
+    return diff <= (atol + rtol * np.abs(b))
+
+
+# locals: c, min, e, cnt, a
+# modifies _in_arc, _next_arc,
+@numba.njit(locals={"a": numba.uint32, "e": numba.uint32})
+def find_entering_arc(
+    pivot_block_size,
+    pivot_next_arc,
+    search_arc_num,
+    state_vector,
+    node_arc_data,
+    in_arc,
+):
+    min = 0
+    cnt = pivot_block_size
+
+    # Pull from tuple for quick reference
+    cost = node_arc_data.cost
+    pi = node_arc_data.pi
+    source = node_arc_data.source
+    target = node_arc_data.target
+
+    for e in range(pivot_next_arc, search_arc_num):
+        c = state_vector[e] * (cost[e] + pi[source[e]] - pi[target[e]])
+        if c < min:
+            min = c
+            in_arc = e
+
+        cnt -= 1
+        if cnt == 0:
+            if np.fabs(pi[source[in_arc]]) > np.fabs(pi[target[in_arc]]):
+                a = np.fabs(pi[source[in_arc]])
+            else:
+                a = np.fabs(pi[target[in_arc]])
+
+            if a <= np.fabs(cost[in_arc]):
+                a = np.fabs(cost[in_arc])
+
+            if min < -(EPSILON * a):
+                pivot_next_arc = e
+                return in_arc, pivot_next_arc
+            else:
+                cnt = pivot_block_size
+
+    for e in range(pivot_next_arc):
+        c = state_vector[e] * (cost[e] + pi[source[e]] - pi[target[e]])
+        if c < min:
+            min = c
+            in_arc = e
+
+        cnt -= 1
+        if cnt == 0:
+            if np.fabs(pi[source[in_arc]]) > np.fabs(pi[target[in_arc]]):
+                a = np.fabs(pi[source[in_arc]])
+            else:
+                a = np.fabs(pi[target[in_arc]])
+
+            if a <= np.fabs(cost[in_arc]):
+                a = np.fabs(cost[in_arc])
+
+            if min < -(EPSILON * a):
+                pivot_next_arc = e
+                return in_arc, pivot_next_arc
+            else:
+                cnt = pivot_block_size
+
+    # assert(pivot_block.next_arc[0] == 0 or e == pivot_block.next_arc[0] - 1)
+
+    if np.fabs(pi[source[in_arc]]) > np.fabs(pi[target[in_arc]]):
+        a = np.fabs(pi[source[in_arc]])
+    else:
+        a = np.fabs(pi[target[in_arc]])
+
+    if a <= np.fabs(cost[in_arc]):
+        a = np.fabs(cost[in_arc])
+
+    if min >= -(EPSILON * a):
+        return -1, 0
+
+    return in_arc, pivot_next_arc
+
+
+# Find the join node
+# Operates with graph (_source, _target) and MST (_succ_num, _parent, in_arc) data
+# locals: u, v
+# modifies: join
+@numba.njit(locals={"u": numba.types.uint16, "v": numba.types.uint16})
+def find_join_node(source, target, succ_num, parent, in_arc):
+    u = source[in_arc]
+    v = target[in_arc]
+    while u != v:
+        if succ_num[u] < succ_num[v]:
+            u = parent[u]
+        else:
+            v = parent[v]
+
+    join = u
+
+    return join
+
+
+# Find the leaving arc of the cycle and returns true if the
+# leaving arc is not the same as the entering arc
+# locals: first, second, result, d, e
+# modifies: u_in, v_in, u_out, delta
+@numba.njit(
+    locals={
+        "u": numba.uint16,
+        "u_in": numba.uint16,
+        "u_out": numba.uint16,
+        "v_in": numba.uint16,
+        "first": numba.uint16,
+        "second": numba.uint16,
+        "result": numba.uint8,
+        "in_arc": numba.uint32,
+    }
+)
+def find_leaving_arc(join, in_arc, node_arc_data, spanning_tree):
+    source = node_arc_data.source
+    target = node_arc_data.target
+    flow = node_arc_data.flow
+
+    state = spanning_tree.state
+    forward = spanning_tree.forward
+    pred = spanning_tree.pred
+    parent = spanning_tree.parent
+
+    u_out = -1  # May not be set, but we need to return something?
+
+    # Initialize first and second nodes according to the direction
+    # of the cycle
+    if state[in_arc] == ArcState.STATE_LOWER:
+        first = source[in_arc]
+        second = target[in_arc]
+    else:
+        first = target[in_arc]
+        second = source[in_arc]
+
+    delta = INFINITY
+    result = 0
+
+    # Search the cycle along the path form the first node to the root
+    u = first
+    while u != join:
+        e = pred[u]
+        if forward[u]:
+            d = flow[e]
+        else:
+            d = INFINITY
+
+        if d < delta:
+            delta = d
+            u_out = u
+            result = 1
+
+        u = parent[u]
+
+    # Search the cycle along the path form the second node to the root
+    u = second
+    while u != join:
+        e = pred[u]
+        if forward[u]:
+            d = INFINITY
+        else:
+            d = flow[e]
+
+        if d <= delta:
+            delta = d
+            u_out = u
+            result = 2
+
+        u = parent[u]
+
+    if result == 1:
+        u_in = first
+        v_in = second
+    else:
+        u_in = second
+        v_in = first
+
+    return LeavingArcData(u_in, u_out, v_in, delta, result != 0)
+
+
+# Change _flow and _state vectors
+# locals: val, u
+# modifies: _state, _flow
+@numba.njit(locals={"u": numba.uint16, "in_arc": numba.uint32, "val": numba.float64})
+def update_flow(join, leaving_arc_data, node_arc_data, spanning_tree, in_arc):
+    source = node_arc_data.source
+    target = node_arc_data.target
+    flow = node_arc_data.flow
+
+    state = spanning_tree.state
+    pred = spanning_tree.pred
+    parent = spanning_tree.parent
+    forward = spanning_tree.forward
+
+    # Augment along the cycle
+    if leaving_arc_data.delta > 0:
+        val = state[in_arc] * leaving_arc_data.delta
+        flow[in_arc] += val
+        u = source[in_arc]
+        while u != join:
+            if forward[u]:
+                flow[pred[u]] -= val
+            else:
+                flow[pred[u]] += val
+
+            u = parent[u]
+
+        u = target[in_arc]
+        while u != join:
+            if forward[u]:
+                flow[pred[u]] += val
+            else:
+                flow[pred[u]] -= val
+
+            u = parent[u]
+
+    # Update the state of the entering and leaving arcs
+    if leaving_arc_data.change:
+        state[in_arc] = ArcState.STATE_TREE
+        if flow[pred[leaving_arc_data.u_out]] == 0:
+            state[pred[leaving_arc_data.u_out]] = ArcState.STATE_LOWER
+        else:
+            state[pred[leaving_arc_data.u_out]] = ArcState.STATE_UPPER
+    else:
+        state[in_arc] = -state[in_arc]
+
+
+# Update the tree structure
+# locals: u, w, old_rev_thread, old_succ_num, old_last_succ, tmp_sc, tmp_ls
+# more locals: up_limit_in, up_limit_out, _dirty_revs
+# modifies: v_out, _thread, _rev_thread, _parent, _last_succ,
+# modifies: _pred, _forward, _succ_num
+@numba.njit(
+    locals={
+        "u": numba.int32,
+        "w": numba.int32,
+        "u_in": numba.uint16,
+        "u_out": numba.uint16,
+        "v_in": numba.uint16,
+        "right": numba.uint16,
+        "stem": numba.uint16,
+        "new_stem": numba.uint16,
+        "par_stem": numba.uint16,
+        "in_arc": numba.uint32,
+    }
+)
+def update_spanning_tree(spanning_tree, leaving_arc_data, join, in_arc, source):
+
+    parent = spanning_tree.parent
+    thread = spanning_tree.thread
+    rev_thread = spanning_tree.rev_thread
+    succ_num = spanning_tree.succ_num
+    last_succ = spanning_tree.last_succ
+    forward = spanning_tree.forward
+    pred = spanning_tree.pred
+
+    u_out = leaving_arc_data.u_out
+    u_in = leaving_arc_data.u_in
+    v_in = leaving_arc_data.v_in
+
+    old_rev_thread = rev_thread[u_out]
+    old_succ_num = succ_num[u_out]
+    old_last_succ = last_succ[u_out]
+    v_out = parent[u_out]
+
+    u = last_succ[u_in]  # the last successor of u_in
+    right = thread[u]  # the node after it
+
+    # Handle the case when old_rev_thread equals to v_in
+    # (it also means that join and v_out coincide)
+    if old_rev_thread == v_in:
+        last = thread[last_succ[u_out]]
+    else:
+        last = thread[v_in]
+
+    # Update _thread and _parent along the stem nodes (i.e. the nodes
+    # between u_in and u_out, whose parent have to be changed)
+    thread[v_in] = stem = u_in
+    dirty_revs = []
+    dirty_revs.append(v_in)
+    par_stem = v_in
+    while stem != u_out:
+        # Insert the next stem node into the thread list
+        new_stem = parent[stem]
+        thread[u] = new_stem
+        dirty_revs.append(u)
+
+        # Remove the subtree of stem from the thread list
+        w = rev_thread[stem]
+        thread[w] = right
+        rev_thread[right] = w
+
+        # Change the parent node and shift stem nodes
+        parent[stem] = par_stem
+        par_stem = stem
+        stem = new_stem
+
+        # Update u and right
+        if last_succ[stem] == last_succ[par_stem]:
+            u = rev_thread[par_stem]
+        else:
+            u = last_succ[stem]
+
+        right = thread[u]
+
+    parent[u_out] = par_stem
+    thread[u] = last
+    rev_thread[last] = u
+    last_succ[u_out] = u
+
+    # Remove the subtree of u_out from the thread list except for
+    # the case when old_rev_thread equals to v_in
+    # (it also means that join and v_out coincide)
+    if old_rev_thread != v_in:
+        thread[old_rev_thread] = right
+        rev_thread[right] = old_rev_thread
+
+    # Update _rev_thread using the new _thread values
+    for i in range(len(dirty_revs)):
+        u = dirty_revs[i]
+        rev_thread[thread[u]] = u
+
+    # Update _pred, _forward, _last_succ and _succ_num for the
+    # stem nodes from u_out to u_in
+    tmp_sc = 0
+    tmp_ls = last_succ[u_out]
+    u = u_out
+    while u != u_in:
+        w = parent[u]
+        pred[u] = pred[w]
+        forward[u] = not forward[w]
+        tmp_sc += succ_num[u] - succ_num[w]
+        succ_num[u] = tmp_sc
+        last_succ[w] = tmp_ls
+        u = w
+
+    pred[u_in] = in_arc
+    forward[u_in] = u_in == source[in_arc]
+    succ_num[u_in] = old_succ_num
+
+    # Set limits for updating _last_succ form v_in and v_out
+    # towards the root
+    up_limit_in = -1
+    up_limit_out = -1
+    if last_succ[join] == v_in:
+        up_limit_out = join
+    else:
+        up_limit_in = join
+
+    # Update _last_succ from v_in towards the root
+    u = v_in
+    while u != up_limit_in and last_succ[u] == v_in:
+        last_succ[u] = last_succ[u_out]
+        u = parent[u]
+
+    # Update _last_succ from v_out towards the root
+    if join != old_rev_thread and v_in != old_rev_thread:
+        u = v_out
+        while u != up_limit_out and last_succ[u] == old_last_succ:
+            last_succ[u] = old_rev_thread
+            u = parent[u]
+
+    else:
+        u = v_out
+        while u != up_limit_out and last_succ[u] == old_last_succ:
+            last_succ[u] = last_succ[u_out]
+            u = parent[u]
+
+    # Update _succ_num from v_in to join
+    u = v_in
+    while u != join:
+        succ_num[u] += old_succ_num
+        u = parent[u]
+
+    # Update _succ_num from v_out to join
+    u = v_out
+    while u != join:
+        succ_num[u] -= old_succ_num
+        u = parent[u]
+
+
+# Update potentials
+# locals: sigma, end
+# modifies: _pi
+@numba.njit(
+    fastmath=True,
+    inline="always",
+    locals={"u": numba.uint16, "u_in": numba.uint16, "v_in": numba.uint16},
+)
+def update_potential(leaving_arc_data, pi, cost, spanning_tree):
+
+    thread = spanning_tree.thread
+    pred = spanning_tree.pred
+    forward = spanning_tree.forward
+    last_succ = spanning_tree.last_succ
+
+    u_in = leaving_arc_data.u_in
+    v_in = leaving_arc_data.v_in
+
+    if forward[u_in]:
+        sigma = pi[v_in] - pi[u_in] - cost[pred[u_in]]
+    else:
+        sigma = pi[v_in] - pi[u_in] + cost[pred[u_in]]
+
+    # Update potentials in the subtree, which has been moved
+    end = thread[last_succ[u_in]]
+    u = u_in
+    while u != end:
+        pi[u] += sigma
+        u = thread[u]
+
+
+# If we have mixed arcs (for better random access)
+# we need a more complicated function to get the ID of a given arc
+@numba.njit()
+def arc_id(arc, graph):
+    k = graph.n_arcs - arc - 1
+    if graph.use_arc_mixing:
+        smallv = (k > graph.num_total_big_subsequence_numbers) & 1
+        k -= graph.num_total_big_subsequence_numbers * smallv
+        subsequence_length2 = graph.subsequence_length - smallv
+        subsequence_num = (
+            k // subsequence_length2
+        ) + graph.num_big_subsequences * smallv
+        subsequence_offset = (k % subsequence_length2) * graph.mixing_coeff
+
+        return subsequence_offset + subsequence_num
+    else:
+        return k
+
+
+# Heuristic initial pivots
+# locals: curr, total, supply_nodes, demand_nodes, u
+# modifies:
+@numba.njit(locals={"i": numba.uint16})
+def construct_initial_pivots(graph, node_arc_data, spanning_tree):
+
+    cost = node_arc_data.cost
+    pi = node_arc_data.pi
+    source = node_arc_data.source
+    target = node_arc_data.target
+    supply = node_arc_data.supply
+
+    n1 = graph.n
+    n2 = graph.m
+    n_nodes = graph.n_nodes
+    n_arcs = graph.n_arcs
+
+    state = spanning_tree.state
+
+    total = 0
+    supply_nodes = []
+    demand_nodes = []
+
+    for u in range(n_nodes):
+        curr = supply[n_nodes - u - 1]  # _node_id(u)
+        if curr > 0:
+            total += curr
+            supply_nodes.append(u)
+        elif curr < 0:
+            demand_nodes.append(u)
+
+    arc_vector = []
+    if len(supply_nodes) == 1 and len(demand_nodes) == 1:
+        # Perform a reverse graph search from the sink to the source
+        reached = np.zeros(n_nodes, dtype=np.bool_)
+        s = supply_nodes[0]
+        t = demand_nodes[0]
+        stack = []
+        reached[t] = True
+        stack.append(t)
+        while len(stack) > 0:
+            u = stack[-1]
+            v = stack[-1]
+            stack.pop(-1)
+            if v == s:
+                break
+
+            first_arc = n_arcs + v - n_nodes if v >= n1 else -1
+            for a in range(first_arc, -1, -n2):
+                u = a // n2
+                if reached[u]:
+                    continue
+
+                j = arc_id(a, graph)
+                if INFINITY >= total:
+                    arc_vector.append(j)
+                    reached[u] = True
+                    stack.append(u)
+
+    else:
+        # Find the min. cost incomming arc for each demand node
+        for i in range(len(demand_nodes)):
+            v = demand_nodes[i]
+            c = MAX
+            min_cost = MAX
+            min_arc = INVALID
+            first_arc = n_arcs + v - n_nodes if v >= n1 else -1
+            for a in range(first_arc, -1, -n2):
+                c = cost[arc_id(a, graph)]
+                if c < min_cost:
+                    min_cost = c
+                    min_arc = a
+
+            if min_arc != INVALID:
+                arc_vector.append(arc_id(min_arc, graph))
+
+    # Perform heuristic initial pivots
+    in_arc = -1
+    for i in range(len(arc_vector)):
+        in_arc = arc_vector[i]
+        # Bad arcs
+        if (
+            state[in_arc] * (cost[in_arc] + pi[source[in_arc]] - pi[target[in_arc]])
+            >= 0
+        ):
+            continue
+
+        join = find_join_node(
+            source, target, spanning_tree.succ_num, spanning_tree.parent, in_arc
+        )
+        leaving_arc_data = find_leaving_arc(join, in_arc, node_arc_data, spanning_tree)
+        if leaving_arc_data.delta >= MAX:
+            return False, in_arc
+
+        update_flow(join, leaving_arc_data, node_arc_data, spanning_tree, in_arc)
+        if leaving_arc_data.change:
+            update_spanning_tree(spanning_tree, leaving_arc_data, join, in_arc, source)
+            update_potential(leaving_arc_data, pi, cost, spanning_tree)
+
+    return True, in_arc
+
+
+@numba.njit()
+def allocate_graph_structures(n, m, use_arc_mixing=True):
+
+    # Size bipartite graph
+    n_nodes = n + m
+    n_arcs = n * m
+
+    # Resize vectors
+    all_node_num = n_nodes + 1
+    max_arc_num = n_arcs + 2 * n_nodes
+    root = n_nodes
+
+    source = np.zeros(max_arc_num, dtype=np.uint16)
+    target = np.zeros(max_arc_num, dtype=np.uint16)
+    cost = np.ones(max_arc_num, dtype=np.float64)
+    supply = np.zeros(all_node_num, dtype=np.float64)
+    flow = np.zeros(max_arc_num, dtype=np.float64)
+    pi = np.zeros(all_node_num, dtype=np.float64)
+
+    parent = np.zeros(all_node_num, dtype=np.int32)
+    pred = np.zeros(all_node_num, dtype=np.int32)
+    forward = np.zeros(all_node_num, dtype=np.bool_)
+    thread = np.zeros(all_node_num, dtype=np.int32)
+    rev_thread = np.zeros(all_node_num, dtype=np.int32)
+    succ_num = np.zeros(all_node_num, dtype=np.int32)
+    last_succ = np.zeros(all_node_num, dtype=np.int32)
+    state = np.zeros(max_arc_num, dtype=np.int8)
+
+    if use_arc_mixing:
+        # Store the arcs in a mixed order
+        k = max(np.int32(np.sqrt(n_arcs)), 10)
+        mixing_coeff = k
+        subsequence_length = (n_arcs // mixing_coeff) + 1
+        num_big_subsequences = n_arcs % mixing_coeff
+        num_total_big_subsequence_numbers = subsequence_length * num_big_subsequences
+
+        i = 0
+        j = 0
+        for a in range(n_arcs - 1, -1, -1):
+            source[i] = n_nodes - (a // m) - 1
+            target[i] = n_nodes - ((a % m) + n) - 1
+            i += k
+            if i >= n_arcs:
+                j += 1
+                i = j
+
+    else:
+        # dummy values
+        subsequence_length = 0
+        mixing_coeff = 0
+        num_big_subsequences = 0
+        num_total_big_subsequence_numbers = 0
+        # Store the arcs in the original order
+        i = 0
+        for a in range(n_arcs - 1, -1, -1):
+            source[i] = n_nodes - (a // m) - 1
+            target[i] = n_nodes - ((a % m) + n) - 1
+            i += 1
+
+    node_arc_data = NodeArcData(cost, supply, flow, pi, source, target)
+    spanning_tree = SpanningTree(
+        parent, pred, thread, rev_thread, succ_num, last_succ, forward, state, root
+    )
+    graph = DiGraph(
+        n_nodes,
+        n_arcs,
+        n,
+        m,
+        use_arc_mixing,
+        num_total_big_subsequence_numbers,
+        subsequence_length,
+        num_big_subsequences,
+        mixing_coeff,
+    )
+
+    return node_arc_data, spanning_tree, graph
+
+
+@numba.njit(locals={"u": numba.uint16, "e": numba.uint32})
+def initialize_graph_structures(graph, node_arc_data, spanning_tree):
+
+    n_nodes = graph.n_nodes
+    n_arcs = graph.n_arcs
+
+    # unpack arrays
+    cost = node_arc_data.cost
+    supply = node_arc_data.supply
+    flow = node_arc_data.flow
+    pi = node_arc_data.pi
+    source = node_arc_data.source
+    target = node_arc_data.target
+
+    parent = spanning_tree.parent
+    pred = spanning_tree.pred
+    thread = spanning_tree.thread
+    rev_thread = spanning_tree.rev_thread
+    succ_num = spanning_tree.succ_num
+    last_succ = spanning_tree.last_succ
+    forward = spanning_tree.forward
+    state = spanning_tree.state
+
+    if n_nodes == 0:
+        return False
+
+    # Check the sum of supply values
+    net_supply = 0
+    for i in range(n_nodes):
+        net_supply += supply[i]
+
+    if np.fabs(net_supply) > NET_SUPPLY_ERROR_TOLERANCE:
+        return False
+
+    # Fix using doubles
+    # Initialize artifical cost
+    artificial_cost = 0.0
+    for i in range(n_arcs):
+        if cost[i] > artificial_cost:
+            artificial_cost = cost[i]
+        # reset flow and state vectors
+        if flow[i] != 0:
+            flow[i] = 0
+        state[i] = ArcState.STATE_LOWER
+
+    artificial_cost = (artificial_cost + 1) * n_nodes
+
+    # Set data for the artificial root node
+    root = n_nodes
+    parent[root] = -1
+    pred[root] = -1
+    thread[root] = 0
+    rev_thread[0] = root
+    succ_num[root] = n_nodes + 1
+    last_succ[root] = root - 1
+    supply[root] = -net_supply
+    pi[root] = 0
+
+    # Add artificial arcs and initialize the spanning tree data structure
+    # EQ supply constraints
+    e = n_arcs
+    for u in range(n_nodes):
+        parent[u] = root
+        pred[u] = e
+        thread[u] = u + 1
+        rev_thread[u + 1] = u
+        succ_num[u] = 1
+        last_succ[u] = u
+        state[e] = ArcState.STATE_TREE
+        if supply[u] >= 0:
+            forward[u] = True
+            pi[u] = 0
+            source[e] = u
+            target[e] = root
+            flow[e] = supply[u]
+            cost[e] = 0
+        else:
+            forward[u] = False
+            pi[u] = artificial_cost
+            source[e] = root
+            target[e] = u
+            flow[e] = -supply[u]
+            cost[e] = artificial_cost
+        e += 1
+
+    return True
+
+
+@numba.njit()
+def initialize_supply(left_node_supply, right_node_supply, graph, supply):
+    for n in range(graph.n_nodes):
+        if n < graph.n:
+            supply[graph.n_nodes - n - 1] = left_node_supply[n]
+        else:
+            supply[graph.n_nodes - n - 1] = right_node_supply[n - graph.n]
+
+
+@numba.njit(inline="always")
+def set_cost(arc, cost_val, cost, graph):
+    cost[arc_id(arc, graph)] = cost_val
+
+
+@numba.njit(locals={"i": numba.uint16, "j": numba.uint16})
+def initialize_cost(cost_matrix, graph, cost):
+    for i in range(cost_matrix.shape[0]):
+        for j in range(cost_matrix.shape[1]):
+            set_cost(i * cost_matrix.shape[1] + j, cost_matrix[i, j], cost, graph)
+
+
+@numba.njit(fastmath=True, locals={"i": numba.uint32})
+def total_cost(flow, cost):
+    c = 0.0
+    for i in range(flow.shape[0]):
+        c += flow[i] * cost[i]
+    return c
+
+
+@numba.njit(nogil=True)
+def network_simplex_core(node_arc_data, spanning_tree, graph, max_iter):
+
+    # pivot_block = PivotBlock(
+    #     max(np.int32(np.sqrt(graph.n_arcs)), 10),
+    #     np.zeros(1, dtype=np.int32),
+    #     graph.n_arcs,
+    # )
+    pivot_block_size = max(np.int32(np.sqrt(graph.n_arcs)), 10)
+    search_arc_num = graph.n_arcs
+    solution_status = ProblemStatus.OPTIMAL
+
+    # Perform heuristic initial pivots
+    bounded, in_arc = construct_initial_pivots(graph, node_arc_data, spanning_tree)
+    if not bounded:
+        return ProblemStatus.UNBOUNDED
+
+    iter_number = 0
+    # pivot.setDantzig(true);
+    # Execute the Network Simplex algorithm
+    in_arc, pivot_next_arc = find_entering_arc(
+        pivot_block_size, 0, search_arc_num, spanning_tree.state, node_arc_data, in_arc
+    )
+    while in_arc >= 0:
+        iter_number += 1
+        if max_iter > 0 and iter_number >= max_iter:
+            solution_status = ProblemStatus.MAX_ITER_REACHED
+            break
+
+        join = find_join_node(
+            node_arc_data.source,
+            node_arc_data.target,
+            spanning_tree.succ_num,
+            spanning_tree.parent,
+            in_arc,
+        )
+        leaving_arc_data = find_leaving_arc(join, in_arc, node_arc_data, spanning_tree)
+        if leaving_arc_data.delta >= MAX:
+            return ProblemStatus.UNBOUNDED
+
+        update_flow(join, leaving_arc_data, node_arc_data, spanning_tree, in_arc)
+
+        if leaving_arc_data.change:
+            update_spanning_tree(
+                spanning_tree, leaving_arc_data, join, in_arc, node_arc_data.source
+            )
+            update_potential(
+                leaving_arc_data, node_arc_data.pi, node_arc_data.cost, spanning_tree
+            )
+
+        in_arc, pivot_next_arc = find_entering_arc(
+            pivot_block_size,
+            pivot_next_arc,
+            search_arc_num,
+            spanning_tree.state,
+            node_arc_data,
+            in_arc,
+        )
+
+    flow = node_arc_data.flow
+    pi = node_arc_data.pi
+
+    # Check feasibility
+    if solution_status == ProblemStatus.OPTIMAL:
+        for e in range(graph.n_arcs, graph.n_arcs + graph.n_nodes):
+            if flow[e] != 0:
+                if np.abs(flow[e]) > EPSILON:
+                    return ProblemStatus.INFEASIBLE
+                else:
+                    flow[e] = 0
+
+    # Shift potentials to meet the requirements of the GEQ/LEQ type
+    # optimality conditions
+    max_pot = -INFINITY
+    for i in range(graph.n_nodes):
+        if pi[i] > max_pot:
+            max_pot = pi[i]
+    if max_pot > 0:
+        for i in range(graph.n_nodes):
+            pi[i] -= max_pot
+
+    return solution_status
+
+
+#######################################################
+# SINKHORN distances in various variations
+#######################################################
+
+
+@numba.njit(
+    fastmath=True,
+    parallel=True,
+    locals={"diff": numba.float32, "result": numba.float32},
+    cache=False,
+)
+def right_marginal_error(u, K, v, y):
+    uK = u @ K
+    result = 0.0
+    for i in numba.prange(uK.shape[0]):
+        diff = y[i] - uK[i] * v[i]
+        result += diff * diff
+    return np.sqrt(result)
+
+
+@numba.njit(
+    fastmath=True,
+    parallel=True,
+    locals={"diff": numba.float32, "result": numba.float32},
+    cache=False,
+)
+def right_marginal_error_batch(u, K, v, y):
+    uK = K.T @ u
+    result = 0.0
+    for i in numba.prange(uK.shape[0]):
+        for j in range(uK.shape[1]):
+            diff = y[j, i] - uK[i, j] * v[i, j]
+            result += diff * diff
+    return np.sqrt(result)
+
+
+@numba.njit(fastmath=True, parallel=True, cache=False)
+def transport_plan(K, u, v):
+    i_dim = K.shape[0]
+    j_dim = K.shape[1]
+    result = np.empty_like(K)
+    for i in numba.prange(i_dim):
+        for j in range(j_dim):
+            result[i, j] = u[i] * K[i, j] * v[j]
+
+    return result
+
+
+@numba.njit(fastmath=True, parallel=True, locals={"result": numba.float32}, cache=False)
+def relative_change_in_plan(old_u, old_v, new_u, new_v):
+    i_dim = old_u.shape[0]
+    j_dim = old_v.shape[0]
+    result = 0.0
+    for i in numba.prange(i_dim):
+        for j in range(j_dim):
+            old_uv = old_u[i] * old_v[j]
+            result += np.float32(np.abs(old_uv - new_u[i] * new_v[j]) / old_uv)
+
+    return result / (i_dim * j_dim)
+
+
+@numba.njit(fastmath=True, parallel=True, cache=False)
+def precompute_K_prime(K, x):
+    i_dim = K.shape[0]
+    j_dim = K.shape[1]
+    result = np.empty_like(K)
+    for i in numba.prange(i_dim):
+        if x[i] > 0.0:
+            x_i_inverse = 1.0 / x[i]
+        else:
+            x_i_inverse = INFINITY
+        for j in range(j_dim):
+            result[i, j] = x_i_inverse * K[i, j]
+
+    return result
+
+
+@numba.njit(fastmath=True, parallel=True, cache=False)
+def K_from_cost(cost, regularization):
+    i_dim = cost.shape[0]
+    j_dim = cost.shape[1]
+    result = np.empty_like(cost)
+    for i in numba.prange(i_dim):
+        for j in range(j_dim):
+            scaled_cost = cost[i, j] / regularization
+            result[i, j] = np.exp(-scaled_cost)
+
+    return result
+
+
+@numba.njit(fastmath=True, cache=True)
+def sinkhorn_iterations(
+    x, y, u, v, K, max_iter=1000, error_tolerance=1e-9, change_tolerance=1e-9
+):
+    K_prime = precompute_K_prime(K, x)
+
+    prev_u = u
+    prev_v = v
+
+    for iteration in range(max_iter):
+
+        next_v = y / (K.T @ u)
+
+        if np.any(~np.isfinite(next_v)):
+            break
+
+        next_u = 1.0 / (K_prime @ next_v)
+
+        if np.any(~np.isfinite(next_u)):
+            break
+
+        u = next_u
+        v = next_v
+
+        if iteration % 20 == 0:
+            # Check if values in plan have changed significantly since last 20 iterations
+            relative_change = relative_change_in_plan(prev_u, prev_v, next_u, next_v)
+            if relative_change <= change_tolerance:
+                break
+
+            prev_u = u
+            prev_v = v
+
+        if iteration % 10 == 0:
+            # Check if right marginal error is less than tolerance every 10 iterations
+            err = right_marginal_error(u, K, v, y)
+            if err <= error_tolerance:
+                break
+
+    return u, v
+
+
+@numba.njit(fastmath=True, cache=True)
+def sinkhorn_iterations_batch(x, y, u, v, K, max_iter=1000, error_tolerance=1e-9):
+    K_prime = precompute_K_prime(K, x)
+
+    for iteration in range(max_iter):
+
+        next_v = y.T / (K.T @ u)
+
+        if np.any(~np.isfinite(next_v)):
+            break
+
+        next_u = 1.0 / (K_prime @ next_v)
+
+        if np.any(~np.isfinite(next_u)):
+            break
+
+        u = next_u
+        v = next_v
+
+        if iteration % 10 == 0:
+            # Check if right marginal error is less than tolerance every 10 iterations
+            err = right_marginal_error_batch(u, K, v, y)
+            if err <= error_tolerance:
+                break
+
+    return u, v
+
+
+@numba.njit(fastmath=True, cache=True)
+def sinkhorn_transport_plan(
+    x,
+    y,
+    cost=_dummy_cost,
+    regularization=1.0,
+    max_iter=1000,
+    error_tolerance=1e-9,
+    change_tolerance=1e-9,
+):
+    dim_x = x.shape[0]
+    dim_y = y.shape[0]
+    u = np.full(dim_x, 1.0 / dim_x, dtype=cost.dtype)
+    v = np.full(dim_y, 1.0 / dim_y, dtype=cost.dtype)
+
+    K = K_from_cost(cost, regularization)
+    u, v = sinkhorn_iterations(
+        x,
+        y,
+        u,
+        v,
+        K,
+        max_iter=max_iter,
+        error_tolerance=error_tolerance,
+        change_tolerance=change_tolerance,
+    )
+
+    return transport_plan(K, u, v)
+
+
+@numba.njit(fastmath=True, cache=True)
+def sinkhorn_distance(x, y, cost=_dummy_cost, regularization=1.0):
+    transport_plan = sinkhorn_transport_plan(
+        x, y, cost=cost, regularization=regularization
+    )
+    dim_i = transport_plan.shape[0]
+    dim_j = transport_plan.shape[1]
+    result = 0.0
+    for i in range(dim_i):
+        for j in range(dim_j):
+            result += transport_plan[i, j] * cost[i, j]
+
+    return result
+
+
+@numba.njit(fastmath=True, parallel=True, cache=False)
+def sinkhorn_distance_batch(x, y, cost=_dummy_cost, regularization=1.0):
+    dim_x = x.shape[0]
+    dim_y = y.shape[0]
+
+    batch_size = y.shape[1]
+
+    u = np.full((dim_x, batch_size), 1.0 / dim_x, dtype=cost.dtype)
+    v = np.full((dim_y, batch_size), 1.0 / dim_y, dtype=cost.dtype)
+
+    K = K_from_cost(cost, regularization)
+    u, v = sinkhorn_iterations_batch(
+        x,
+        y,
+        u,
+        v,
+        K,
+    )
+
+    i_dim = K.shape[0]
+    j_dim = K.shape[1]
+    result = np.zeros(batch_size)
+    for i in range(i_dim):
+        for j in range(j_dim):
+            K_times_cost = K[i, j] * cost[i, j]
+            for batch in range(batch_size):
+                result[batch] += u[i, batch] * K_times_cost * v[j, batch]
+
+    return result
+
+
+def make_fixed_cost_sinkhorn_distance(cost, regularization=1.0):
+
+    K = K_from_cost(cost, regularization)
+    dim_x = K.shape[0]
+    dim_y = K.shape[1]
+
+    @numba.njit(fastmath=True)
+    def closure(x, y):
+        u = np.full(dim_x, 1.0 / dim_x, dtype=cost.dtype)
+        v = np.full(dim_y, 1.0 / dim_y, dtype=cost.dtype)
+
+        K = K_from_cost(cost, regularization)
+        u, v = sinkhorn_iterations(
+            x,
+            y,
+            u,
+            v,
+            K,
+        )
+
+        current_plan = transport_plan(K, u, v)
+
+        result = 0.0
+        for i in range(dim_x):
+            for j in range(dim_y):
+                result += current_plan[i, j] * cost[i, j]
+
+        return result
+
+    return closure

shared/package-files/32/320cceeab3e51f6f4515bee7920fbb652418523bd0f769be82a892f9bab9676c/blob

@@ -0,0 +1,33 @@
+from collections import UserList
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Sequence, Union
+
+from django.core.exceptions import ValidationError
+from django.utils.safestring import SafeText
+
+def pretty_name(name: str) -> str: ...
+def flatatt(attrs: Dict[str, Any]) -> SafeText: ...
+
+class ErrorDict(dict):
+    def as_data(self) -> Dict[str, List[ValidationError]]: ...
+    def get_json_data(self, escape_html: bool = ...) -> Dict[str, Any]: ...
+    def as_json(self, escape_html: bool = ...) -> str: ...
+    def as_ul(self) -> str: ...
+    def as_text(self) -> str: ...
+
+class ErrorList(UserList):
+    data: List[Union[ValidationError, str]]
+    error_class: str = ...
+    def __init__(
+        self,
+        initlist: Optional[Union[ErrorList, Sequence[Union[str, Exception]]]] = ...,
+        error_class: Optional[str] = ...,
+    ) -> None: ...
+    def as_data(self) -> List[ValidationError]: ...
+    def get_json_data(self, escape_html: bool = ...) -> List[Dict[str, str]]: ...
+    def as_json(self, escape_html: bool = ...) -> str: ...
+    def as_ul(self) -> str: ...
+    def as_text(self) -> str: ...
+
+def from_current_timezone(value: datetime) -> datetime: ...
+def to_current_timezone(value: datetime) -> datetime: ...

shared/package-files/32/320de8e22b5d74767e87fabe35eacbdff562c3ae498f6fcec9628f7fc895e0e9/blob

@@ -0,0 +1,493 @@
+import contextlib
+import fnmatch
+import hashlib
+import logging
+import os
+import re
+import shutil
+import sys
+import warnings as _warnings
+from concurrent.futures import Executor, ProcessPoolExecutor, ThreadPoolExecutor
+from errno import EACCES, ENOENT, EPERM, EROFS
+from itertools import chain
+from os.path import (
+    abspath,
+    basename,
+    dirname,
+    isdir,
+    isfile,
+    islink,
+    join,
+    lexists,
+    normpath,
+    split,
+)
+from stat import S_IEXEC, S_IMODE, S_ISDIR, S_ISREG, S_IWRITE
+from subprocess import STDOUT, CalledProcessError, check_output, list2cmdline
+from tempfile import NamedTemporaryFile, mkdtemp
+
+on_win = sys.platform == "win32"
+log = logging.getLogger(__file__)
+CONDA_TEMP_EXTENSION = ".c~"
+
+
+def which(executable):
+    from distutils.spawn import find_executable
+
+    return find_executable(executable)
+
+
+def make_writable(path):
+    try:
+        mode = os.lstat(path).st_mode
+        if S_ISDIR(mode):
+            os.chmod(path, S_IMODE(mode) | S_IWRITE | S_IEXEC)
+        elif S_ISREG(mode) or islink(path):
+            os.chmod(path, S_IMODE(mode) | S_IWRITE)
+        else:
+            log.debug("path cannot be made writable: %s", path)
+        return True
+    except Exception as e:
+        eno = getattr(e, "errno", None)
+        if eno in (ENOENT,):
+            log.debug("tried to make writable, but didn't exist: %s", path)
+            raise
+        elif eno in (EACCES, EPERM, EROFS):
+            log.debug("tried make writable but failed: %s\n%r", path, e)
+            return False
+        else:
+            log.warn("Error making path writable: %s\n%r", path, e)
+            raise
+
+
+class DummyExecutor(Executor):
+    def map(self, func, *iterables):
+        for iterable in iterables:
+            for thing in iterable:
+                yield func(thing)
+
+
+def get_executor(processes):
+    return DummyExecutor() if processes == 1 else ProcessPoolExecutor(max_workers=processes)
+
+
+def recursive_make_writable(path):
+    # The need for this function was pointed out at
+    #   https://github.com/conda/conda/issues/3266#issuecomment-239241915
+    # Especially on windows, file removal will often fail because it is marked read-only
+    if isdir(path):
+        for root, dirs, files in os.walk(path):
+            for path in chain.from_iterable((files, dirs)):
+                try:
+                    make_writable(join(root, path))
+                except:
+                    pass
+    else:
+        try:
+            make_writable(path)
+        except:
+            pass
+
+
+def quote_for_shell(arguments, shell=None):
+    if not shell:
+        shell = "cmd.exe" if on_win else "bash"
+    if shell == "cmd.exe":
+        return list2cmdline(arguments)
+    else:
+        # If any multiline argument gets mixed with any other argument (which is true if we've
+        # arrived in this function) then we just quote it. This assumes something like:
+        # ['python', '-c', 'a\nmultiline\nprogram\n']
+        # It may make sense to allow specifying a replacement character for '\n' too? e.g. ';'
+        quoted = []
+        # This could all be replaced with some regex wizardry but that is less readable and
+        # for code like this, readability is very important.
+        for arg in arguments:
+            if '"' in arg:
+                quote = "'"
+            elif "'" in arg:
+                quote = '"'
+            elif not any(_ in arg for _ in (" ", "\n")):
+                quote = ""
+            else:
+                quote = '"'
+            quoted.append(quote + arg + quote)
+        return " ".join(quoted)
+
+
+def rmtree(path, *args, **kwargs):
+    # subprocessing to delete large folders can be quite a bit faster
+    path = normpath(path)
+    if on_win:
+        try:
+            # the fastest way seems to be using DEL to recursively delete files
+            # https://www.ghacks.net/2017/07/18/how-to-delete-large-folders-in-windows-super-fast/
+            # However, this is not entirely safe, as it can end up following symlinks to folders
+            # https://superuser.com/a/306618/184799
+            # so, we stick with the slower, but hopefully safer way.  Maybe if we figured out how
+            #    to scan for any possible symlinks, we could do the faster way.
+            # out = check_output('DEL /F/Q/S *.* > NUL 2> NUL'.format(path), shell=True,
+            #                    stderr=STDOUT, cwd=path)
+
+            out = check_output(f'RD /S /Q "{path}" > NUL 2> NUL', shell=True, stderr=STDOUT)
+        except:
+            try:
+                # Try to delete in Unicode
+                name = None
+
+                with NamedTemporaryFile(suffix=".bat", delete=False) as batch_file:
+                    batch_file.write(f"RD /S {quote_for_shell([path])}\n")
+                    batch_file.write("chcp 65001\n")
+                    batch_file.write(f"RD /S {quote_for_shell([path])}\n")
+                    batch_file.write("EXIT 0\n")
+                    name = batch_file.name
+                # If the above is bugged we can end up deleting hard-drives, so we check
+                # that 'path' appears in it. This is not bulletproof but it could save you (me).
+                with open(name) as contents:
+                    content = contents.read()
+                    assert path in content
+                comspec = os.environ["COMSPEC"]
+                CREATE_NO_WINDOW = 0x08000000
+                # It is essential that we `pass stdout=None, stderr=None, stdin=None` here because
+                # if we do not, then the standard console handles get attached and chcp affects the
+                # parent process (and any which share those console handles!)
+                out = check_output(
+                    [comspec, "/d", "/c", name],
+                    shell=False,
+                    stdout=None,
+                    stderr=None,
+                    stdin=None,
+                    creationflags=CREATE_NO_WINDOW,
+                )
+
+            except CalledProcessError as e:
+                if e.returncode != 5:
+                    log.error(f"Removing folder {path} the fast way failed.  Output was: {out}")
+                    raise
+                else:
+                    log.debug(f"removing dir contents the fast way failed.  Output was: {out}")
+    else:
+        try:
+            os.makedirs(".empty")
+        except:
+            pass
+        # yes, this looks strange.  See
+        #    https://unix.stackexchange.com/a/79656/34459
+        #    https://web.archive.org/web/20130929001850/http://linuxnote.net/jianingy/en/linux/a-fast-way-to-remove-huge-number-of-files.html  # NOQA
+        rsync = which("rsync")
+        if rsync and isdir(".empty"):
+            try:
+                out = check_output(
+                    [
+                        rsync,
+                        "-a",
+                        "--force",
+                        "--delete",
+                        join(os.getcwd(), ".empty") + "/",
+                        path + "/",
+                    ],
+                    stderr=STDOUT,
+                )
+            except CalledProcessError:
+                log.debug(f"removing dir contents the fast way failed.  Output was: {out}")
+            shutil.rmtree(".empty")
+    shutil.rmtree(path)
+
+
+def unlink_or_rename_to_trash(path):
+    """If files are in use, especially on windows, we can't remove them.
+    The fallback path is to rename them (but keep their folder the same),
+    which maintains the file handle validity.  See comments at:
+    https://serverfault.com/a/503769
+    """
+    try:
+        make_writable(path)
+        os.unlink(path)
+    except OSError:
+        try:
+            os.rename(path, path + ".conda_trash")
+        except OSError:
+            if on_win:
+                # on windows, it is important to use the rename program, as just using python's
+                #    rename leads to permission errors when files are in use.
+                with NamedTemporaryFile(suffix=".bat") as trash_script:
+                    with open(trash_script, "w") as f:
+                        f.write('@pushd "%1"\n')
+                        f.write("@REM Rename src to dest")
+                        f.write('@ren "%2" "%3" > NUL 2> NUL")')
+
+                    _dirname, _fn = split(path)
+                    dest_fn = path + ".conda_trash"
+                    counter = 1
+                    while isfile(dest_fn):
+                        dest_fn = dest_fn.splitext[0] + f".conda_trash_{counter}"
+                        counter += 1
+                    out = "< empty >"
+                    try:
+                        out = check_output(
+                            [
+                                "cmd.exe",
+                                "/C",
+                                trash_script,
+                                _dirname,
+                                _fn,
+                                basename(dest_fn),
+                            ],
+                            stderr=STDOUT,
+                        )
+                    except CalledProcessError:
+                        log.warn(
+                            "renaming file path {} to trash failed.  Output was: {}".format(
+                                path, out
+                            )
+                        )
+
+            log.warn(
+                "Could not remove or rename {}.  Please remove this file manually (you "
+                "may need to reboot to free file handles)".format(path)
+            )
+
+
+def remove_empty_parent_paths(path):
+    # recurse to clean up empty folders that were created to have a nested hierarchy
+    parent_path = dirname(path)
+    while isdir(parent_path) and not os.listdir(parent_path):
+        rmdir(parent_path)
+        parent_path = dirname(parent_path)
+
+
+def rm_rf(path, clean_empty_parents=False, *args, **kw):
+    """
+    Completely delete path
+    max_retries is the number of times to retry on failure. The default is 5. This only applies
+    to deleting a directory.
+    If removing path fails and trash is True, files will be moved to the trash directory.
+    """
+    recursive_make_writable(path)
+    try:
+        path = abspath(path)
+        if isdir(path) and not islink(path):
+            rmdir(path)
+        elif lexists(path):
+            unlink_or_rename_to_trash(path)
+        else:
+            log.debug("rm_rf failed. Not a link, file, or directory: %s", path)
+    finally:
+        if lexists(path):
+            log.info("rm_rf failed for %s", path)
+            return False
+    if isdir(path):
+        delete_trash(path)
+    if clean_empty_parents:
+        remove_empty_parent_paths(path)
+    return True
+
+
+# aliases that all do the same thing (legacy compat)
+try_rmdir_all_empty = move_to_trash = move_path_to_trash = rm_rf
+
+
+def delete_trash(prefix):
+    if not prefix:
+        prefix = sys.prefix
+    exclude = {"envs"}
+    for root, dirs, files in os.walk(prefix, topdown=True):
+        dirs[:] = [d for d in dirs if d not in exclude]
+        for fn in files:
+            if fnmatch.fnmatch(fn, "*.conda_trash*") or fnmatch.fnmatch(
+                fn, "*" + CONDA_TEMP_EXTENSION
+            ):
+                filename = join(root, fn)
+                try:
+                    os.unlink(filename)
+                    remove_empty_parent_paths(filename)
+                except OSError as e:
+                    log.debug("%r errno %d\nCannot unlink %s.", e, e.errno, filename)
+
+
+def rmdir(dirpath):
+    if not isdir(dirpath):
+        return
+    try:
+        rmtree(dirpath)
+    # we don't really care about errors that much.  We'll catch remaining files
+    #    with slower python logic.
+    except:
+        pass
+
+    for root, dirs, files in os.walk(dirpath, topdown=False):
+        for f in files:
+            unlink_or_rename_to_trash(join(root, f))
+
+
+# we have our own TemporaryDirectory class because it's faster and handles disk issues better.
+class TemporaryDirectory:
+    """Create and return a temporary directory.  This has the same
+    behavior as mkdtemp but can be used as a context manager.  For
+    example:
+
+        with TemporaryDirectory() as tmpdir:
+            ...
+
+    Upon exiting the context, the directory and everything contained
+    in it are removed.
+    """
+
+    # Handle mkdtemp raising an exception
+    name = None
+    _closed = False
+
+    def __init__(self, suffix="", prefix=".cph_tmp", dir=os.getcwd()):
+        self.name = mkdtemp(suffix, prefix, dir)
+
+    def __repr__(self):
+        return f"<{self.__class__.__name__} {self.name!r}>"
+
+    def __enter__(self):
+        return self.name
+
+    def cleanup(self, _warn=False, _warnings=_warnings):
+        if self.name and not self._closed:
+            try:
+                rm_rf(self.name)
+            except:
+                _warnings.warn(
+                    'Conda-package-handling says: "I tried to clean up, '
+                    "but I could not.  There is a mess in %s that you might "
+                    'want to clean up yourself.  Sorry..."' % self.name
+                )
+            self._closed = True
+            if _warn and _warnings.warn:
+                _warnings.warn(
+                    f"Implicitly cleaning up {self!r}",
+                    _warnings.ResourceWarning,
+                )
+
+    def __exit__(self, exc, value, tb):
+        self.cleanup()
+
+    def __del__(self):
+        # Issue a ResourceWarning if implicit cleanup needed
+        self.cleanup(_warn=True)
+
+
+@contextlib.contextmanager
+def tmp_chdir(dest):
+    curdir = os.getcwd()
+    try:
+        os.chdir(dest)
+        yield
+    finally:
+        os.chdir(curdir)
+
+
+def ensure_list(arg):
+    if isinstance(arg, str) or not hasattr(arg, "__iter__"):
+        if arg is not None:
+            arg = [arg]
+        else:
+            arg = []
+    return arg
+
+
+def filter_files(
+    files_list,
+    prefix,
+    filter_patterns=(
+        r"(.*[\\\\/])?\.git[\\\\/].*",
+        r"(.*[\\\\/])?\.git$",
+        r"(.*)?\.DS_Store.*",
+        r".*\.la$",
+        r"conda-meta.*",
+    ),
+):
+    """Remove things like the .git directory from the list of files to be copied"""
+    for pattern in filter_patterns:
+        r = re.compile(pattern)
+        files_list = set(files_list) - set(filter(r.match, files_list))
+    return [
+        f
+        for f in files_list
+        if
+        # `islink` prevents symlinks to directories from being removed
+        os.path.islink(os.path.join(prefix, f)) or not os.path.isdir(os.path.join(prefix, f))
+    ]
+
+
+def filter_info_files(files_list, prefix):
+    return filter_files(
+        files_list,
+        prefix,
+        filter_patterns=(
+            "info[\\\\/]index\\.json",
+            "info[\\\\/]files",
+            "info[\\\\/]paths\\.json",
+            "info[\\\\/]about\\.json",
+            "info[\\\\/]has_prefix",
+            "info[\\\\/]hash_input_files",  # legacy, not used anymore
+            "info[\\\\/]hash_input\\.json",
+            "info[\\\\/]run_exports\\.yaml",  # legacy
+            "info[\\\\/]run_exports\\.json",  # current
+            "info[\\\\/]git",
+            "info[\\\\/]recipe[\\\\/].*",
+            "info[\\\\/]recipe_log.json",
+            "info[\\\\/]recipe.tar",
+            "info[\\\\/]test[\\\\/].*",
+            "info[\\\\/]LICENSE.*",
+            "info[\\\\/]requires",
+            "info[\\\\/]meta",
+            "info[\\\\/]platform",
+            "info[\\\\/]no_link",
+            "info[\\\\/]link\\.json",
+            "info[\\\\/]icon\\.png",
+        ),
+    )
+
+
+def _checksum(fd, algorithm, buffersize=65536):
+    hash_impl = getattr(hashlib, algorithm)
+    if not hash_impl:
+        raise ValueError(f"Unrecognized hash algorithm: {algorithm}")
+    else:
+        hash_impl = hash_impl()
+    for block in iter(lambda: fd.read(buffersize), b""):
+        hash_impl.update(block)
+    return hash_impl.hexdigest()
+
+
+def sha256_checksum(fd):
+    return _checksum(fd, "sha256")
+
+
+def md5_checksum(fd):
+    return _checksum(fd, "md5")
+
+
+def checksum(fn, algorithm, buffersize=1 << 18):
+    """
+    Calculate a checksum for a filename (not an open file).
+    """
+    with open(fn, "rb") as fd:
+        return _checksum(fd, algorithm, buffersize)
+
+
+def checksums(fn, algorithms, buffersize=1 << 18):
+    """
+    Calculate multiple checksums for a filename in parallel.
+    """
+    with ThreadPoolExecutor(max_workers=len(algorithms)) as e:
+        # take care not to share hash_impl between threads
+        results = [e.submit(checksum, fn, algorithm, buffersize) for algorithm in algorithms]
+    return [result.result() for result in results]
+
+
+def anonymize_tarinfo(tarinfo):
+    """
+    Remove user id, name from tarinfo.
+    """
+    # also remove timestamps?
+    tarinfo.uid = 0
+    tarinfo.uname = ""
+    tarinfo.gid = 0
+    tarinfo.gname = ""
+    return tarinfo

shared/package-files/32/320e1d8e583d6c7c1f5a26bb913b1315d62a8ec594ca23ff52b9a517218214c9/blob

@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_cslt_compress_ops.h>
+
+namespace at {
+
+
+// aten::_cslt_compress(Tensor input) -> Tensor
+inline at::Tensor _cslt_compress(const at::Tensor & input) {
+    return at::_ops::_cslt_compress::call(input);
+}
+
+}

shared/package-files/32/320f996f1f43e0a3bf44729c901672b9a07966798be64ff26f0f953aff41021a/blob

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor cudnn_affine_grid_generator(const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W);
+
+} // namespace cuda
+} // namespace at

shared/package-files/32/32108975cdae6500033fd995ff28642facdcfabeb25e22f82894d3f488d0c868/blob

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_sparse_csr_prod_ops.h>
+
+namespace at {
+
+
+// aten::_sparse_csr_prod.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
+inline at::Tensor _sparse_csr_prod(const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false, c10::optional<at::ScalarType> dtype=c10::nullopt) {
+    return at::_ops::_sparse_csr_prod_dim_dtype::call(self, dim, keepdim, dtype);
+}
+
+// aten::_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _sparse_csr_prod_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false, c10::optional<at::ScalarType> dtype=c10::nullopt) {
+    return at::_ops::_sparse_csr_prod_dim_dtype_out::call(self, dim, keepdim, dtype, out);
+}
+// aten::_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _sparse_csr_prod_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional<at::ScalarType> dtype, at::Tensor & out) {
+    return at::_ops::_sparse_csr_prod_dim_dtype_out::call(self, dim, keepdim, dtype, out);
+}
+
+}

shared/package-files/32/3211959beba2d9a5e95409567047d8ff854d4f9e77d8e623d030a34943ac516e/blob

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3211959beba2d9a5e95409567047d8ff854d4f9e77d8e623d030a34943ac516e
+size 14798681

shared/package-files/32/321245e3a66997b61f2214325015e61b9a64398230c37092ae487220afc52f9f/blob

@@ -0,0 +1,136 @@
+"""
+@generated by mypy-protobuf.  Do not edit manually!
+isort:skip_file
+"""
+from google.protobuf.descriptor import (
+    Descriptor as google___protobuf___descriptor___Descriptor,
+    EnumDescriptor as google___protobuf___descriptor___EnumDescriptor,
+    FileDescriptor as google___protobuf___descriptor___FileDescriptor,
+)
+
+from google.protobuf.descriptor_pb2 import (
+    FileDescriptorProto as google___protobuf___descriptor_pb2___FileDescriptorProto,
+    GeneratedCodeInfo as google___protobuf___descriptor_pb2___GeneratedCodeInfo,
+)
+
+from google.protobuf.internal.containers import (
+    RepeatedCompositeFieldContainer as google___protobuf___internal___containers___RepeatedCompositeFieldContainer,
+    RepeatedScalarFieldContainer as google___protobuf___internal___containers___RepeatedScalarFieldContainer,
+)
+
+from google.protobuf.internal.enum_type_wrapper import (
+    _EnumTypeWrapper as google___protobuf___internal___enum_type_wrapper____EnumTypeWrapper,
+)
+
+from google.protobuf.message import (
+    Message as google___protobuf___message___Message,
+)
+
+from typing import (
+    Iterable as typing___Iterable,
+    NewType as typing___NewType,
+    Optional as typing___Optional,
+    Text as typing___Text,
+    cast as typing___cast,
+)
+
+from typing_extensions import (
+    Literal as typing_extensions___Literal,
+)
+
+
+builtin___bool = bool
+builtin___bytes = bytes
+builtin___float = float
+builtin___int = int
+
+
+DESCRIPTOR: google___protobuf___descriptor___FileDescriptor = ...
+
+class Version(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    major: builtin___int = ...
+    minor: builtin___int = ...
+    patch: builtin___int = ...
+    suffix: typing___Text = ...
+
+    def __init__(self,
+        *,
+        major : typing___Optional[builtin___int] = None,
+        minor : typing___Optional[builtin___int] = None,
+        patch : typing___Optional[builtin___int] = None,
+        suffix : typing___Optional[typing___Text] = None,
+        ) -> None: ...
+    def HasField(self, field_name: typing_extensions___Literal[u"major",b"major",u"minor",b"minor",u"patch",b"patch",u"suffix",b"suffix"]) -> builtin___bool: ...
+    def ClearField(self, field_name: typing_extensions___Literal[u"major",b"major",u"minor",b"minor",u"patch",b"patch",u"suffix",b"suffix"]) -> None: ...
+type___Version = Version
+
+class CodeGeneratorRequest(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    file_to_generate: google___protobuf___internal___containers___RepeatedScalarFieldContainer[typing___Text] = ...
+    parameter: typing___Text = ...
+
+    @property
+    def proto_file(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[google___protobuf___descriptor_pb2___FileDescriptorProto]: ...
+
+    @property
+    def compiler_version(self) -> type___Version: ...
+
+    def __init__(self,
+        *,
+        file_to_generate : typing___Optional[typing___Iterable[typing___Text]] = None,
+        parameter : typing___Optional[typing___Text] = None,
+        proto_file : typing___Optional[typing___Iterable[google___protobuf___descriptor_pb2___FileDescriptorProto]] = None,
+        compiler_version : typing___Optional[type___Version] = None,
+        ) -> None: ...
+    def HasField(self, field_name: typing_extensions___Literal[u"compiler_version",b"compiler_version",u"parameter",b"parameter"]) -> builtin___bool: ...
+    def ClearField(self, field_name: typing_extensions___Literal[u"compiler_version",b"compiler_version",u"file_to_generate",b"file_to_generate",u"parameter",b"parameter",u"proto_file",b"proto_file"]) -> None: ...
+type___CodeGeneratorRequest = CodeGeneratorRequest
+
+class CodeGeneratorResponse(google___protobuf___message___Message):
+    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+    FeatureValue = typing___NewType('FeatureValue', builtin___int)
+    type___FeatureValue = FeatureValue
+    Feature: _Feature
+    class _Feature(google___protobuf___internal___enum_type_wrapper____EnumTypeWrapper[CodeGeneratorResponse.FeatureValue]):
+        DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ...
+        FEATURE_NONE = typing___cast(CodeGeneratorResponse.FeatureValue, 0)
+        FEATURE_PROTO3_OPTIONAL = typing___cast(CodeGeneratorResponse.FeatureValue, 1)
+    FEATURE_NONE = typing___cast(CodeGeneratorResponse.FeatureValue, 0)
+    FEATURE_PROTO3_OPTIONAL = typing___cast(CodeGeneratorResponse.FeatureValue, 1)
+
+    class File(google___protobuf___message___Message):
+        DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
+        name: typing___Text = ...
+        insertion_point: typing___Text = ...
+        content: typing___Text = ...
+
+        @property
+        def generated_code_info(self) -> google___protobuf___descriptor_pb2___GeneratedCodeInfo: ...
+
+        def __init__(self,
+            *,
+            name : typing___Optional[typing___Text] = None,
+            insertion_point : typing___Optional[typing___Text] = None,
+            content : typing___Optional[typing___Text] = None,
+            generated_code_info : typing___Optional[google___protobuf___descriptor_pb2___GeneratedCodeInfo] = None,
+            ) -> None: ...
+        def HasField(self, field_name: typing_extensions___Literal[u"content",b"content",u"generated_code_info",b"generated_code_info",u"insertion_point",b"insertion_point",u"name",b"name"]) -> builtin___bool: ...
+        def ClearField(self, field_name: typing_extensions___Literal[u"content",b"content",u"generated_code_info",b"generated_code_info",u"insertion_point",b"insertion_point",u"name",b"name"]) -> None: ...
+    type___File = File
+
+    error: typing___Text = ...
+    supported_features: builtin___int = ...
+
+    @property
+    def file(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___CodeGeneratorResponse.File]: ...
+
+    def __init__(self,
+        *,
+        error : typing___Optional[typing___Text] = None,
+        supported_features : typing___Optional[builtin___int] = None,
+        file : typing___Optional[typing___Iterable[type___CodeGeneratorResponse.File]] = None,
+        ) -> None: ...
+    def HasField(self, field_name: typing_extensions___Literal[u"error",b"error",u"supported_features",b"supported_features"]) -> builtin___bool: ...
+    def ClearField(self, field_name: typing_extensions___Literal[u"error",b"error",u"file",b"file",u"supported_features",b"supported_features"]) -> None: ...
+type___CodeGeneratorResponse = CodeGeneratorResponse

shared/package-files/32/3212543684feef77fea9442b687e6b7f67d93e5144135aa44fd5f0180e4c0591/blob

@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/slice_backward_ops.h>
+
+namespace at {
+
+
+// aten::slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor
+inline at::Tensor slice_backward(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) {
+    return at::_ops::slice_backward::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor slice_backward(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) {
+    return at::_ops::slice_backward::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step);
+  }
+}
+
+// aten::slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor
+inline at::Tensor slice_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) {
+    return at::_ops::slice_backward::call(grad_output, input_sizes, dim, start, end, step);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor slice_backward(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) {
+    return at::_ops::slice_backward::call(grad_output, input_sizes, dim, start, end, step);
+  }
+}
+
+// aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & slice_backward_out(at::Tensor & out, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) {
+    return at::_ops::slice_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & slice_backward_out(at::Tensor & out, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) {
+    return at::_ops::slice_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out);
+  }
+}
+
+// aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & slice_backward_outf(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step, at::Tensor & out) {
+    return at::_ops::slice_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & slice_backward_outf(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step, at::Tensor & out) {
+    return at::_ops::slice_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out);
+  }
+}
+
+// aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & slice_backward_symint_out(at::Tensor & out, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) {
+    return at::_ops::slice_backward_out::call(grad_output, input_sizes, dim, start, end, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & slice_backward_out(at::Tensor & out, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) {
+    return at::_ops::slice_backward_out::call(grad_output, input_sizes, dim, start, end, step, out);
+  }
+}
+
+// aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & slice_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step, at::Tensor & out) {
+    return at::_ops::slice_backward_out::call(grad_output, input_sizes, dim, start, end, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & slice_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step, at::Tensor & out) {
+    return at::_ops::slice_backward_out::call(grad_output, input_sizes, dim, start, end, step, out);
+  }
+}
+
+}

shared/package-files/32/321333b3eaad9ecbf633186bc625d4a60c4c736def0fa00665add2ab899eecb1/blob

@@ -0,0 +1,294 @@
+"Test replace, coverage 78%."
+
+from idlelib.replace import ReplaceDialog
+import unittest
+from test.support import requires
+requires('gui')
+from tkinter import Tk, Text
+
+from unittest.mock import Mock
+from idlelib.idle_test.mock_tk import Mbox
+import idlelib.searchengine as se
+
+orig_mbox = se.messagebox
+showerror = Mbox.showerror
+
+
+class ReplaceDialogTest(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        cls.root = Tk()
+        cls.root.withdraw()
+        se.messagebox = Mbox
+        cls.engine = se.SearchEngine(cls.root)
+        cls.dialog = ReplaceDialog(cls.root, cls.engine)
+        cls.dialog.bell = lambda: None
+        cls.dialog.ok = Mock()
+        cls.text = Text(cls.root)
+        cls.text.undo_block_start = Mock()
+        cls.text.undo_block_stop = Mock()
+        cls.dialog.text = cls.text
+
+    @classmethod
+    def tearDownClass(cls):
+        se.messagebox = orig_mbox
+        del cls.text, cls.dialog, cls.engine
+        cls.root.destroy()
+        del cls.root
+
+    def setUp(self):
+        self.text.insert('insert', 'This is a sample sTring')
+
+    def tearDown(self):
+        self.engine.patvar.set('')
+        self.dialog.replvar.set('')
+        self.engine.wordvar.set(False)
+        self.engine.casevar.set(False)
+        self.engine.revar.set(False)
+        self.engine.wrapvar.set(True)
+        self.engine.backvar.set(False)
+        showerror.title = ''
+        showerror.message = ''
+        self.text.delete('1.0', 'end')
+
+    def test_replace_simple(self):
+        # Test replace function with all options at default setting.
+        # Wrap around - True
+        # Regular Expression - False
+        # Match case - False
+        # Match word - False
+        # Direction - Forwards
+        text = self.text
+        equal = self.assertEqual
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace = self.dialog.replace_it
+
+        # test accessor method
+        self.engine.setpat('asdf')
+        equal(self.engine.getpat(), pv.get())
+
+        # text found and replaced
+        pv.set('a')
+        rv.set('asdf')
+        replace()
+        equal(text.get('1.8', '1.12'), 'asdf')
+
+        # don't "match word" case
+        text.mark_set('insert', '1.0')
+        pv.set('is')
+        rv.set('hello')
+        replace()
+        equal(text.get('1.2', '1.7'), 'hello')
+
+        # don't "match case" case
+        pv.set('string')
+        rv.set('world')
+        replace()
+        equal(text.get('1.23', '1.28'), 'world')
+
+        # without "regular expression" case
+        text.mark_set('insert', 'end')
+        text.insert('insert', '\nline42:')
+        before_text = text.get('1.0', 'end')
+        pv.set(r'[a-z][\d]+')
+        replace()
+        after_text = text.get('1.0', 'end')
+        equal(before_text, after_text)
+
+        # test with wrap around selected and complete a cycle
+        text.mark_set('insert', '1.9')
+        pv.set('i')
+        rv.set('j')
+        replace()
+        equal(text.get('1.8'), 'i')
+        equal(text.get('2.1'), 'j')
+        replace()
+        equal(text.get('2.1'), 'j')
+        equal(text.get('1.8'), 'j')
+        before_text = text.get('1.0', 'end')
+        replace()
+        after_text = text.get('1.0', 'end')
+        equal(before_text, after_text)
+
+        # text not found
+        before_text = text.get('1.0', 'end')
+        pv.set('foobar')
+        replace()
+        after_text = text.get('1.0', 'end')
+        equal(before_text, after_text)
+
+        # test access method
+        self.dialog.find_it(0)
+
+    def test_replace_wrap_around(self):
+        text = self.text
+        equal = self.assertEqual
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace = self.dialog.replace_it
+        self.engine.wrapvar.set(False)
+
+        # replace candidate found both after and before 'insert'
+        text.mark_set('insert', '1.4')
+        pv.set('i')
+        rv.set('j')
+        replace()
+        equal(text.get('1.2'), 'i')
+        equal(text.get('1.5'), 'j')
+        replace()
+        equal(text.get('1.2'), 'i')
+        equal(text.get('1.20'), 'j')
+        replace()
+        equal(text.get('1.2'), 'i')
+
+        # replace candidate found only before 'insert'
+        text.mark_set('insert', '1.8')
+        pv.set('is')
+        before_text = text.get('1.0', 'end')
+        replace()
+        after_text = text.get('1.0', 'end')
+        equal(before_text, after_text)
+
+    def test_replace_whole_word(self):
+        text = self.text
+        equal = self.assertEqual
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace = self.dialog.replace_it
+        self.engine.wordvar.set(True)
+
+        pv.set('is')
+        rv.set('hello')
+        replace()
+        equal(text.get('1.0', '1.4'), 'This')
+        equal(text.get('1.5', '1.10'), 'hello')
+
+    def test_replace_match_case(self):
+        equal = self.assertEqual
+        text = self.text
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace = self.dialog.replace_it
+        self.engine.casevar.set(True)
+
+        before_text = self.text.get('1.0', 'end')
+        pv.set('this')
+        rv.set('that')
+        replace()
+        after_text = self.text.get('1.0', 'end')
+        equal(before_text, after_text)
+
+        pv.set('This')
+        replace()
+        equal(text.get('1.0', '1.4'), 'that')
+
+    def test_replace_regex(self):
+        equal = self.assertEqual
+        text = self.text
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace = self.dialog.replace_it
+        self.engine.revar.set(True)
+
+        before_text = text.get('1.0', 'end')
+        pv.set(r'[a-z][\d]+')
+        rv.set('hello')
+        replace()
+        after_text = text.get('1.0', 'end')
+        equal(before_text, after_text)
+
+        text.insert('insert', '\nline42')
+        replace()
+        equal(text.get('2.0', '2.8'), 'linhello')
+
+        pv.set('')
+        replace()
+        self.assertIn('error', showerror.title)
+        self.assertIn('Empty', showerror.message)
+
+        pv.set(r'[\d')
+        replace()
+        self.assertIn('error', showerror.title)
+        self.assertIn('Pattern', showerror.message)
+
+        showerror.title = ''
+        showerror.message = ''
+        pv.set('[a]')
+        rv.set('test\\')
+        replace()
+        self.assertIn('error', showerror.title)
+        self.assertIn('Invalid Replace Expression', showerror.message)
+
+        # test access method
+        self.engine.setcookedpat("?")
+        equal(pv.get(), "\\?")
+
+    def test_replace_backwards(self):
+        equal = self.assertEqual
+        text = self.text
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace = self.dialog.replace_it
+        self.engine.backvar.set(True)
+
+        text.insert('insert', '\nis as ')
+
+        pv.set('is')
+        rv.set('was')
+        replace()
+        equal(text.get('1.2', '1.4'), 'is')
+        equal(text.get('2.0', '2.3'), 'was')
+        replace()
+        equal(text.get('1.5', '1.8'), 'was')
+        replace()
+        equal(text.get('1.2', '1.5'), 'was')
+
+    def test_replace_all(self):
+        text = self.text
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace_all = self.dialog.replace_all
+
+        text.insert('insert', '\n')
+        text.insert('insert', text.get('1.0', 'end')*100)
+        pv.set('is')
+        rv.set('was')
+        replace_all()
+        self.assertNotIn('is', text.get('1.0', 'end'))
+
+        self.engine.revar.set(True)
+        pv.set('')
+        replace_all()
+        self.assertIn('error', showerror.title)
+        self.assertIn('Empty', showerror.message)
+
+        pv.set('[s][T]')
+        rv.set('\\')
+        replace_all()
+
+        self.engine.revar.set(False)
+        pv.set('text which is not present')
+        rv.set('foobar')
+        replace_all()
+
+    def test_default_command(self):
+        text = self.text
+        pv = self.engine.patvar
+        rv = self.dialog.replvar
+        replace_find = self.dialog.default_command
+        equal = self.assertEqual
+
+        pv.set('This')
+        rv.set('was')
+        replace_find()
+        equal(text.get('sel.first', 'sel.last'), 'was')
+
+        self.engine.revar.set(True)
+        pv.set('')
+        replace_find()
+
+
+if __name__ == '__main__':
+    unittest.main(verbosity=2)

shared/package-files/32/3216fd20e5c0bf873a9e4936e10438cd135dbfc15c60a373707cc7d98e35312f/blob

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API binary_cross_entropy_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, const c10::optional<at::Tensor> &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::binary_cross_entropy_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "binary_cross_entropy_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor")
+  static at::Tensor call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction);
+};
+
+struct TORCH_API binary_cross_entropy_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Tensor &, const c10::optional<at::Tensor> &, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::binary_cross_entropy_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "grad_input")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "binary_cross_entropy_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & grad_input);
+};
+
+}} // namespace at::_ops

shared/package-files/32/32183bd6f9e38dbe1f9b48d98230e8596f2b0093017a1f1a0c233be608c4a4a4/blob

@@ -0,0 +1,228 @@
+from sympy.core.add import Add
+from sympy.core.expr import unchanged
+from sympy.core.mul import Mul
+from sympy.core.symbol import symbols
+from sympy.core.relational import Eq
+from sympy.concrete.summations import Sum
+from sympy.functions.elementary.complexes import im, re
+from sympy.functions.elementary.piecewise import Piecewise
+from sympy.matrices.immutable import ImmutableDenseMatrix
+from sympy.matrices.expressions.matexpr import MatrixSymbol
+from sympy.matrices.expressions.matadd import MatAdd
+from sympy.matrices.expressions.special import (
+    ZeroMatrix, GenericZeroMatrix, Identity, GenericIdentity, OneMatrix)
+from sympy.matrices.expressions.matmul import MatMul
+from sympy.testing.pytest import raises
+
+
+def test_zero_matrix_creation():
+    assert unchanged(ZeroMatrix, 2, 2)
+    assert unchanged(ZeroMatrix, 0, 0)
+    raises(ValueError, lambda: ZeroMatrix(-1, 2))
+    raises(ValueError, lambda: ZeroMatrix(2.0, 2))
+    raises(ValueError, lambda: ZeroMatrix(2j, 2))
+    raises(ValueError, lambda: ZeroMatrix(2, -1))
+    raises(ValueError, lambda: ZeroMatrix(2, 2.0))
+    raises(ValueError, lambda: ZeroMatrix(2, 2j))
+
+    n = symbols('n')
+    assert unchanged(ZeroMatrix, n, n)
+    n = symbols('n', integer=False)
+    raises(ValueError, lambda: ZeroMatrix(n, n))
+    n = symbols('n', negative=True)
+    raises(ValueError, lambda: ZeroMatrix(n, n))
+
+
+def test_generic_zero_matrix():
+    z = GenericZeroMatrix()
+    n = symbols('n', integer=True)
+    A = MatrixSymbol("A", n, n)
+
+    assert z == z
+    assert z != A
+    assert A != z
+
+    assert z.is_ZeroMatrix
+
+    raises(TypeError, lambda: z.shape)
+    raises(TypeError, lambda: z.rows)
+    raises(TypeError, lambda: z.cols)
+
+    assert MatAdd() == z
+    assert MatAdd(z, A) == MatAdd(A)
+    # Make sure it is hashable
+    hash(z)
+
+
+def test_identity_matrix_creation():
+    assert Identity(2)
+    assert Identity(0)
+    raises(ValueError, lambda: Identity(-1))
+    raises(ValueError, lambda: Identity(2.0))
+    raises(ValueError, lambda: Identity(2j))
+
+    n = symbols('n')
+    assert Identity(n)
+    n = symbols('n', integer=False)
+    raises(ValueError, lambda: Identity(n))
+    n = symbols('n', negative=True)
+    raises(ValueError, lambda: Identity(n))
+
+
+def test_generic_identity():
+    I = GenericIdentity()
+    n = symbols('n', integer=True)
+    A = MatrixSymbol("A", n, n)
+
+    assert I == I
+    assert I != A
+    assert A != I
+
+    assert I.is_Identity
+    assert I**-1 == I
+
+    raises(TypeError, lambda: I.shape)
+    raises(TypeError, lambda: I.rows)
+    raises(TypeError, lambda: I.cols)
+
+    assert MatMul() == I
+    assert MatMul(I, A) == MatMul(A)
+    # Make sure it is hashable
+    hash(I)
+
+
+def test_one_matrix_creation():
+    assert OneMatrix(2, 2)
+    assert OneMatrix(0, 0)
+    assert Eq(OneMatrix(1, 1), Identity(1))
+    raises(ValueError, lambda: OneMatrix(-1, 2))
+    raises(ValueError, lambda: OneMatrix(2.0, 2))
+    raises(ValueError, lambda: OneMatrix(2j, 2))
+    raises(ValueError, lambda: OneMatrix(2, -1))
+    raises(ValueError, lambda: OneMatrix(2, 2.0))
+    raises(ValueError, lambda: OneMatrix(2, 2j))
+
+    n = symbols('n')
+    assert OneMatrix(n, n)
+    n = symbols('n', integer=False)
+    raises(ValueError, lambda: OneMatrix(n, n))
+    n = symbols('n', negative=True)
+    raises(ValueError, lambda: OneMatrix(n, n))
+
+
+def test_ZeroMatrix():
+    n, m = symbols('n m', integer=True)
+    A = MatrixSymbol('A', n, m)
+    Z = ZeroMatrix(n, m)
+
+    assert A + Z == A
+    assert A*Z.T == ZeroMatrix(n, n)
+    assert Z*A.T == ZeroMatrix(n, n)
+    assert A - A == ZeroMatrix(*A.shape)
+
+    assert Z
+
+    assert Z.transpose() == ZeroMatrix(m, n)
+    assert Z.conjugate() == Z
+    assert Z.adjoint() == ZeroMatrix(m, n)
+    assert re(Z) == Z
+    assert im(Z) == Z
+
+    assert ZeroMatrix(n, n)**0 == Identity(n)
+    assert ZeroMatrix(3, 3).as_explicit() == ImmutableDenseMatrix.zeros(3, 3)
+
+
+def test_ZeroMatrix_doit():
+    n = symbols('n', integer=True)
+    Znn = ZeroMatrix(Add(n, n, evaluate=False), n)
+    assert isinstance(Znn.rows, Add)
+    assert Znn.doit() == ZeroMatrix(2*n, n)
+    assert isinstance(Znn.doit().rows, Mul)
+
+
+def test_OneMatrix():
+    n, m = symbols('n m', integer=True)
+    A = MatrixSymbol('A', n, m)
+    U = OneMatrix(n, m)
+
+    assert U.shape == (n, m)
+    assert isinstance(A + U, Add)
+    assert U.transpose() == OneMatrix(m, n)
+    assert U.conjugate() == U
+    assert U.adjoint() == OneMatrix(m, n)
+    assert re(U) == U
+    assert im(U) == ZeroMatrix(n, m)
+
+    assert OneMatrix(n, n) ** 0 == Identity(n)
+
+    U = OneMatrix(n, n)
+    assert U[1, 2] == 1
+
+    U = OneMatrix(2, 3)
+    assert U.as_explicit() == ImmutableDenseMatrix.ones(2, 3)
+
+
+def test_OneMatrix_doit():
+    n = symbols('n', integer=True)
+    Unn = OneMatrix(Add(n, n, evaluate=False), n)
+    assert isinstance(Unn.rows, Add)
+    assert Unn.doit() == OneMatrix(2 * n, n)
+    assert isinstance(Unn.doit().rows, Mul)
+
+
+def test_OneMatrix_mul():
+    n, m, k = symbols('n m k', integer=True)
+    w = MatrixSymbol('w', n, 1)
+    assert OneMatrix(n, m) * OneMatrix(m, k) == OneMatrix(n, k) * m
+    assert w * OneMatrix(1, 1) == w
+    assert OneMatrix(1, 1) * w.T == w.T
+
+
+def test_Identity():
+    n, m = symbols('n m', integer=True)
+    A = MatrixSymbol('A', n, m)
+    i, j = symbols('i j')
+
+    In = Identity(n)
+    Im = Identity(m)
+
+    assert A*Im == A
+    assert In*A == A
+
+    assert In.transpose() == In
+    assert In.inverse() == In
+    assert In.conjugate() == In
+    assert In.adjoint() == In
+    assert re(In) == In
+    assert im(In) == ZeroMatrix(n, n)
+
+    assert In[i, j] != 0
+    assert Sum(In[i, j], (i, 0, n-1), (j, 0, n-1)).subs(n,3).doit() == 3
+    assert Sum(Sum(In[i, j], (i, 0, n-1)), (j, 0, n-1)).subs(n,3).doit() == 3
+
+    # If range exceeds the limit `(0, n-1)`, do not remove `Piecewise`:
+    expr = Sum(In[i, j], (i, 0, n-1))
+    assert expr.doit() == 1
+    expr = Sum(In[i, j], (i, 0, n-2))
+    assert expr.doit().dummy_eq(
+        Piecewise(
+            (1, (j >= 0) & (j <= n-2)),
+            (0, True)
+        )
+    )
+    expr = Sum(In[i, j], (i, 1, n-1))
+    assert expr.doit().dummy_eq(
+        Piecewise(
+            (1, (j >= 1) & (j <= n-1)),
+            (0, True)
+        )
+    )
+    assert Identity(3).as_explicit() == ImmutableDenseMatrix.eye(3)
+
+
+def test_Identity_doit():
+    n = symbols('n', integer=True)
+    Inn = Identity(Add(n, n, evaluate=False))
+    assert isinstance(Inn.rows, Add)
+    assert Inn.doit() == Identity(2*n)
+    assert isinstance(Inn.doit().rows, Mul)

shared/package-files/32/32191fb149da315122ea846b71f4e0a7a875ff54024bc6efc6dc2813a6ea652e/blob

@@ -0,0 +1,22 @@
+from typing import Optional
+
+class AESCCM(object):
+    def __init__(self, key: bytes, tag_length: Optional[int]) -> None: ...
+    def decrypt(self, nonce: bytes, data: bytes, associated_data: Optional[bytes]) -> bytes: ...
+    def encrypt(self, nonce: bytes, data: bytes, associated_data: Optional[bytes]) -> bytes: ...
+    @classmethod
+    def generate_key(cls, bit_length: int) -> bytes: ...
+
+class AESGCM(object):
+    def __init__(self, key: bytes) -> None: ...
+    def decrypt(self, nonce: bytes, data: bytes, associated_data: Optional[bytes]) -> bytes: ...
+    def encrypt(self, nonce: bytes, data: bytes, associated_data: Optional[bytes]) -> bytes: ...
+    @classmethod
+    def generate_key(cls, bit_length: int) -> bytes: ...
+
+class ChaCha20Poly1305(object):
+    def __init__(self, key: bytes) -> None: ...
+    def decrypt(self, nonce: bytes, data: bytes, associated_data: Optional[bytes]) -> bytes: ...
+    def encrypt(self, nonce: bytes, data: bytes, associated_data: Optional[bytes]) -> bytes: ...
+    @classmethod
+    def generate_key(cls) -> bytes: ...

shared/package-files/32/3219b2a5be56cf119d05a429c9269ef4d6e21499659047b3762b631da8dded10/blob

@@ -0,0 +1,87 @@
+"""
+    pygments.lexers.solidity
+    ~~~~~~~~~~~~~~~~~~~~~~~~
+
+    Lexers for Solidity.
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+from pygments.lexer import RegexLexer, bygroups, include, words
+from pygments.token import Text, Comment, Operator, Keyword, Name, String, \
+    Number, Punctuation, Whitespace
+
+__all__ = ['SolidityLexer']
+
+
+class SolidityLexer(RegexLexer):
+    """
+    For Solidity source code.
+
+    .. versionadded:: 2.5
+    """
+
+    name = 'Solidity'
+    aliases = ['solidity']
+    filenames = ['*.sol']
+    mimetypes = []
+
+    datatype = (
+        r'\b(address|bool|(?:(?:bytes|hash|int|string|uint)(?:8|16|24|32|40|48|56|64'
+        r'|72|80|88|96|104|112|120|128|136|144|152|160|168|176|184|192|200|208'
+        r'|216|224|232|240|248|256)?))\b'
+    )
+
+    tokens = {
+        'root': [
+            include('whitespace'),
+            include('comments'),
+            (r'\bpragma\s+solidity\b', Keyword, 'pragma'),
+            (r'\b(contract)(\s+)([a-zA-Z_]\w*)',
+             bygroups(Keyword, Whitespace, Name.Entity)),
+            (datatype + r'(\s+)((?:external|public|internal|private)\s+)?' +
+             r'([a-zA-Z_]\w*)',
+             bygroups(Keyword.Type, Whitespace, Keyword, Name.Variable)),
+            (r'\b(enum|event|function|struct)(\s+)([a-zA-Z_]\w*)',
+             bygroups(Keyword.Type, Whitespace, Name.Variable)),
+            (r'\b(msg|block|tx)\.([A-Za-z_][a-zA-Z0-9_]*)\b', Keyword),
+            (words((
+                'block', 'break', 'constant', 'constructor', 'continue',
+                'contract', 'do', 'else', 'external', 'false', 'for',
+                'function', 'if', 'import', 'inherited', 'internal', 'is',
+                'library', 'mapping', 'memory', 'modifier', 'msg', 'new',
+                'payable', 'private', 'public', 'require', 'return',
+                'returns', 'struct', 'suicide', 'throw', 'this', 'true',
+                'tx', 'var', 'while'), prefix=r'\b', suffix=r'\b'),
+             Keyword.Type),
+            (words(('keccak256',), prefix=r'\b', suffix=r'\b'), Name.Builtin),
+            (datatype, Keyword.Type),
+            include('constants'),
+            (r'[a-zA-Z_]\w*', Text),
+            (r'[~!%^&*+=|?:<>/-]', Operator),
+            (r'[.;{}(),\[\]]', Punctuation)
+        ],
+        'comments': [
+            (r'//(\n|[\w\W]*?[^\\]\n)', Comment.Single),
+            (r'/(\\\n)?[*][\w\W]*?[*](\\\n)?/', Comment.Multiline),
+            (r'/(\\\n)?[*][\w\W]*', Comment.Multiline)
+        ],
+        'constants': [
+            (r'("(\\"|.)*?")', String.Double),
+            (r"('(\\'|.)*?')", String.Single),
+            (r'\b0[xX][0-9a-fA-F]+\b', Number.Hex),
+            (r'\b\d+\b', Number.Decimal),
+        ],
+        'pragma': [
+            include('whitespace'),
+            include('comments'),
+            (r'(\^|>=|<)(\s*)(\d+\.\d+\.\d+)',
+             bygroups(Operator, Whitespace, Keyword)),
+            (r';', Punctuation, '#pop')
+        ],
+        'whitespace': [
+            (r'\s+', Whitespace),
+            (r'\n', Whitespace)
+        ]
+    }

shared/package-files/32/321f0293b95d07de6fd2df7c9da5fee13bd9bbb8ae47241731c526ba427c1e2f/blob

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:321f0293b95d07de6fd2df7c9da5fee13bd9bbb8ae47241731c526ba427c1e2f
+size 436640

shared/package-files/32/3221d56abebe6de504615129a97391217e1ff06dbd238ec3dfa92b7a5b5d7e84/blob

@@ -0,0 +1,33 @@
+from numpy.distutils.fcompiler import FCompiler
+
+compilers = ['PathScaleFCompiler']
+
+class PathScaleFCompiler(FCompiler):
+
+    compiler_type = 'pathf95'
+    description = 'PathScale Fortran Compiler'
+    version_pattern =  r'PathScale\(TM\) Compiler Suite: Version (?P<version>[\d.]+)'
+
+    executables = {
+        'version_cmd'  : ["pathf95", "-version"],
+        'compiler_f77' : ["pathf95", "-fixedform"],
+        'compiler_fix' : ["pathf95", "-fixedform"],
+        'compiler_f90' : ["pathf95"],
+        'linker_so'    : ["pathf95", "-shared"],
+        'archiver'     : ["ar", "-cr"],
+        'ranlib'       : ["ranlib"]
+    }
+    pic_flags = ['-fPIC']
+    module_dir_switch = '-module ' # Don't remove ending space!
+    module_include_switch = '-I'
+
+    def get_flags_opt(self):
+        return ['-O3']
+    def get_flags_debug(self):
+        return ['-g']
+
+if __name__ == '__main__':
+    from distutils import log
+    log.set_verbosity(2)
+    from numpy.distutils import customized_fcompiler
+    print(customized_fcompiler(compiler='pathf95').get_version())

shared/package-files/32/32258d53e28c9d2c075b977388a296199f18363315d016297765aac4f57cec8f/blob

@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_has_same_storage_numel_ops.h>
+
+namespace at {
+
+
+// aten::_has_same_storage_numel(Tensor self, Tensor other) -> bool
+inline bool _has_same_storage_numel(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::_has_same_storage_numel::call(self, other);
+}
+
+}

shared/package-files/32/322745165c7c739c69422e50ecd36bf6e1b991115dc19348c6dec887aba48817/blob

@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> min(const at::Tensor & self, at::Dimname dim, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> min_out(at::Tensor & min, at::Tensor & min_indices, const at::Tensor & self, at::Dimname dim, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> min_outf(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & min, at::Tensor & min_indices);
+TORCH_API at::Tensor min(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & min_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & min_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at

shared/package-files/32/3227f4f6071f45f20edae842a8d37d382fcd5e080a8b36eec1acfa49c1b33141/blob

@@ -0,0 +1,125 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DatetimeIndex,
+    Index,
+    date_range,
+    factorize,
+)
+import pandas._testing as tm
+
+
+class TestDatetimeIndexFactorize:
+    def test_factorize(self):
+        idx1 = DatetimeIndex(
+            ["2014-01", "2014-01", "2014-02", "2014-02", "2014-03", "2014-03"]
+        )
+
+        exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp)
+        exp_idx = DatetimeIndex(["2014-01", "2014-02", "2014-03"])
+
+        arr, idx = idx1.factorize()
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, exp_idx)
+        assert idx.freq == exp_idx.freq
+
+        arr, idx = idx1.factorize(sort=True)
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, exp_idx)
+        assert idx.freq == exp_idx.freq
+
+        # tz must be preserved
+        idx1 = idx1.tz_localize("Asia/Tokyo")
+        exp_idx = exp_idx.tz_localize("Asia/Tokyo")
+
+        arr, idx = idx1.factorize()
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, exp_idx)
+        assert idx.freq == exp_idx.freq
+
+        idx2 = DatetimeIndex(
+            ["2014-03", "2014-03", "2014-02", "2014-01", "2014-03", "2014-01"]
+        )
+
+        exp_arr = np.array([2, 2, 1, 0, 2, 0], dtype=np.intp)
+        exp_idx = DatetimeIndex(["2014-01", "2014-02", "2014-03"])
+        arr, idx = idx2.factorize(sort=True)
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, exp_idx)
+        assert idx.freq == exp_idx.freq
+
+        exp_arr = np.array([0, 0, 1, 2, 0, 2], dtype=np.intp)
+        exp_idx = DatetimeIndex(["2014-03", "2014-02", "2014-01"])
+        arr, idx = idx2.factorize()
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, exp_idx)
+        assert idx.freq == exp_idx.freq
+
+    def test_factorize_preserves_freq(self):
+        # GH#38120 freq should be preserved
+        idx3 = date_range("2000-01", periods=4, freq="ME", tz="Asia/Tokyo")
+        exp_arr = np.array([0, 1, 2, 3], dtype=np.intp)
+
+        arr, idx = idx3.factorize()
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, idx3)
+        assert idx.freq == idx3.freq
+
+        arr, idx = factorize(idx3)
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        tm.assert_index_equal(idx, idx3)
+        assert idx.freq == idx3.freq
+
+    def test_factorize_tz(self, tz_naive_fixture, index_or_series):
+        tz = tz_naive_fixture
+        # GH#13750
+        base = date_range("2016-11-05", freq="h", periods=100, tz=tz)
+        idx = base.repeat(5)
+
+        exp_arr = np.arange(100, dtype=np.intp).repeat(5)
+
+        obj = index_or_series(idx)
+
+        arr, res = obj.factorize()
+        tm.assert_numpy_array_equal(arr, exp_arr)
+        expected = base._with_freq(None)
+        tm.assert_index_equal(res, expected)
+        assert res.freq == expected.freq
+
+    def test_factorize_dst(self, index_or_series):
+        # GH#13750
+        idx = date_range("2016-11-06", freq="h", periods=12, tz="US/Eastern")
+        obj = index_or_series(idx)
+
+        arr, res = obj.factorize()
+        tm.assert_numpy_array_equal(arr, np.arange(12, dtype=np.intp))
+        tm.assert_index_equal(res, idx)
+        if index_or_series is Index:
+            assert res.freq == idx.freq
+
+        idx = date_range("2016-06-13", freq="h", periods=12, tz="US/Eastern")
+        obj = index_or_series(idx)
+
+        arr, res = obj.factorize()
+        tm.assert_numpy_array_equal(arr, np.arange(12, dtype=np.intp))
+        tm.assert_index_equal(res, idx)
+        if index_or_series is Index:
+            assert res.freq == idx.freq
+
+    @pytest.mark.parametrize("sort", [True, False])
+    def test_factorize_no_freq_non_nano(self, tz_naive_fixture, sort):
+        # GH#51978 case that does not go through the fastpath based on
+        #  non-None freq
+        tz = tz_naive_fixture
+        idx = date_range("2016-11-06", freq="h", periods=5, tz=tz)[[0, 4, 1, 3, 2]]
+        exp_codes, exp_uniques = idx.factorize(sort=sort)
+
+        res_codes, res_uniques = idx.as_unit("s").factorize(sort=sort)
+
+        tm.assert_numpy_array_equal(res_codes, exp_codes)
+        tm.assert_index_equal(res_uniques, exp_uniques.as_unit("s"))
+
+        res_codes, res_uniques = idx.as_unit("s").to_series().factorize(sort=sort)
+        tm.assert_numpy_array_equal(res_codes, exp_codes)
+        tm.assert_index_equal(res_uniques, exp_uniques.as_unit("s"))

shared/package-files/32/322a98cc2207bcd6cc1fe2dd4582effb84f410803b8432ade462d1d084532dd2/blob

@@ -0,0 +1,75 @@
+import logging
+import os
+import tempfile
+import shutil
+import json
+from subprocess import check_call, check_output
+from tarfile import TarFile
+
+from dateutil.zoneinfo import METADATA_FN, ZONEFILENAME
+
+
+def rebuild(filename, tag=None, format="gz", zonegroups=[], metadata=None):
+    """Rebuild the internal timezone info in dateutil/zoneinfo/zoneinfo*tar*
+
+    filename is the timezone tarball from ``ftp.iana.org/tz``.
+
+    """
+    tmpdir = tempfile.mkdtemp()
+    zonedir = os.path.join(tmpdir, "zoneinfo")
+    moduledir = os.path.dirname(__file__)
+    try:
+        with TarFile.open(filename) as tf:
+            for name in zonegroups:
+                tf.extract(name, tmpdir)
+            filepaths = [os.path.join(tmpdir, n) for n in zonegroups]
+
+            _run_zic(zonedir, filepaths)
+
+        # write metadata file
+        with open(os.path.join(zonedir, METADATA_FN), 'w') as f:
+            json.dump(metadata, f, indent=4, sort_keys=True)
+        target = os.path.join(moduledir, ZONEFILENAME)
+        with TarFile.open(target, "w:%s" % format) as tf:
+            for entry in os.listdir(zonedir):
+                entrypath = os.path.join(zonedir, entry)
+                tf.add(entrypath, entry)
+    finally:
+        shutil.rmtree(tmpdir)
+
+
+def _run_zic(zonedir, filepaths):
+    """Calls the ``zic`` compiler in a compatible way to get a "fat" binary.
+
+    Recent versions of ``zic`` default to ``-b slim``, while older versions
+    don't even have the ``-b`` option (but default to "fat" binaries). The
+    current version of dateutil does not support Version 2+ TZif files, which
+    causes problems when used in conjunction with "slim" binaries, so this
+    function is used to ensure that we always get a "fat" binary.
+    """
+
+    try:
+        help_text = check_output(["zic", "--help"])
+    except OSError as e:
+        _print_on_nosuchfile(e)
+        raise
+
+    if b"-b " in help_text:
+        bloat_args = ["-b", "fat"]
+    else:
+        bloat_args = []
+
+    check_call(["zic"] + bloat_args + ["-d", zonedir] + filepaths)
+
+
+def _print_on_nosuchfile(e):
+    """Print helpful troubleshooting message
+
+    e is an exception raised by subprocess.check_call()
+
+    """
+    if e.errno == 2:
+        logging.error(
+            "Could not find zic. Perhaps you need to install "
+            "libc-bin or some other package that provides it, "
+            "or it's not in your PATH?")