Add files using upload-large-folder tool

parrot/lib/python3.10/site-packages/gradio_client-0.2.9.dist-info/METADATA

@@ -0,0 +1,178 @@
+Metadata-Version: 2.1
+Name: gradio_client
+Version: 0.2.9
+Summary: Python library for easily interacting with trained machine learning models
+Project-URL: Homepage, https://github.com/gradio-app/gradio
+Author-email: Abubakar Abid <team@gradio.app>, Ali Abid <team@gradio.app>, Ali Abdalla <team@gradio.app>, Dawood Khan <team@gradio.app>, Ahsen Khaliq <team@gradio.app>, Pete Allen <team@gradio.app>, Freddy Boulton <team@gradio.app>
+License-Expression: Apache-2.0
+Keywords: API,client,machine learning
+Classifier: Development Status :: 4 - Beta
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Software Development :: User Interfaces
+Requires-Python: >=3.8
+Requires-Dist: fsspec
+Requires-Dist: httpx
+Requires-Dist: huggingface-hub>=0.13.0
+Requires-Dist: packaging
+Requires-Dist: requests
+Requires-Dist: typing-extensions
+Requires-Dist: websockets
+Description-Content-Type: text/markdown
+
+# `gradio_client`: Use a Gradio app as an API -- in 3 lines of Python
+
+This directory contains the source code for `gradio_client`, a lightweight Python library that makes it very easy to use any Gradio app as an API. 
+
+As an example, consider this [Hugging Face Space that transcribes audio files](https://huggingface.co/spaces/abidlabs/whisper) that are recorded from the microphone.
+
+![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/whisper-screenshot.jpg)
+
+Using the `gradio_client` library, we can easily use the Gradio as an API to transcribe audio files programmatically.
+
+Here's the entire code to do it:
+
+```python
+from gradio_client import Client
+
+client = Client("abidlabs/whisper") 
+client.predict("audio_sample.wav")  
+
+>> "This is a test of the whisper speech recognition model."
+```
+
+The Gradio client works with any Gradio Space, whether it be an image generator, a stateful chatbot, or a tax calculator.
+
+## Installation
+
+If you already have a recent version of `gradio`, then the `gradio_client` is included as a dependency. 
+
+Otherwise, the lightweight `gradio_client` package can be installed from pip (or pip3) and works with Python versions 3.8 or higher:
+
+```bash
+$ pip install gradio_client
+```
+
+## Basic Usage
+
+### Connecting to a Space or a Gradio app
+
+Start by connecting instantiating a `Client` object and connecting it to a Gradio app that is running on Spaces (or anywhere else)!
+
+**Connecting to a Space**
+
+```python
+from gradio_client import Client
+
+client = Client("abidlabs/en2fr")  # a Space that translates from English to French
+```
+
+You can also connect to private Spaces by passing in your HF token with the `hf_token` parameter. You can get your HF token here: https://huggingface.co/settings/tokens
+
+```python
+from gradio_client import Client
+
+client = Client("abidlabs/my-private-space", hf_token="...") 
+```
+
+**Duplicating a Space for private use**
+
+While you can use any public Space as an API, you may get rate limited by Hugging Face if you make too many requests. For unlimited usage of a Space, simply duplicate the Space to create a private Space,
+and then use it to make as many requests as you'd like!
+
+The `gradio_client` includes a class method: `Client.duplicate()` to make this process simple:
+
+```python
+from gradio_client import Client
+
+client = Client.duplicate("abidlabs/whisper") 
+client.predict("audio_sample.wav")  
+
+>> "This is a test of the whisper speech recognition model."
+```
+
+If you have previously duplicated a Space, re-running `duplicate()` will *not* create a new Space. Instead, the Client will attach to the previously-created Space. So it is safe to re-run the `Client.duplicate()` method multiple times. 
+
+**Note:** if the original Space uses GPUs, your private Space will as well, and your Hugging Face account will get billed based on the price of the GPU. To minimize charges, your Space will automatically go to sleep after 1 hour of inactivity. You can also set the hardware using the `hardware` parameter of `duplicate()`.
+
+
+**Connecting a general Gradio app**
+
+If your app is running somewhere else, just provide the full URL instead, including the "http://" or "https://". Here's an example of making predictions to a Gradio app that is running on a share URL:
+
+```python
+from gradio_client import Client
+
+client = Client("https://bec81a83-5b5c-471e.gradio.live")
+```
+
+
+### Inspecting the API endpoints
+
+Once you have connected to a Gradio app, you can view the APIs that are available to you by calling the `.view_api()` method. For the Whisper Space, we see the following:
+
+```
+Client.predict() Usage Info
+---------------------------
+Named API endpoints: 1
+
+ - predict(input_audio, api_name="/predict") -> value_0
+    Parameters:
+     - [Audio] input_audio: str (filepath or URL)
+    Returns:
+     - [Textbox] value_0: str (value)
+```
+
+This shows us that we have 1 API endpoint in this space, and shows us how to use the API endpoint to make a prediction: we should call the `.predict()` method, providing a parameter `input_audio` of type `str`, which is a `filepath or URL`. 
+
+We should also provide the `api_name='/predict'` argument. Although this isn't necessary if a Gradio app has a single named endpoint, it does allow us to call different endpoints in a single app if they are available. If an app has unnamed API endpoints, these can also be displayed by running `.view_api(all_endpoints=True)`.
+
+
+### Making a prediction
+
+The simplest way to make a prediction is simply to call the `.predict()` function with the appropriate arguments:
+
+```python
+from gradio_client import Client
+
+client = Client("abidlabs/en2fr")
+client.predict("Hello")
+
+>> Bonjour
+```
+
+If there are multiple parameters, then you should pass them as separate arguments to `.predict()`, like this:
+
+
+```python
+from gradio_client import Client
+
+client = Client("gradio/calculator")
+client.predict(4, "add", 5)
+
+>> 9.0
+```
+
+For certain inputs, such as images, you should pass in the filepath or URL to the file. Likewise, for the corresponding output types, you will get a filepath or URL returned. 
+
+```python
+from gradio_client import Client
+
+client = Client("abidlabs/whisper")
+client.predict("https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3")
+
+>> "My thought I have nobody by a beauty and will as you poured. Mr. Rochester is serve in that so don't find simpus, and devoted abode, to at might in a r—"
+```
+
+
+## Advanced Usage
+
+For more ways to use the Gradio Python Client, check out our dedicated Guide on the Python client, available here: https://www.gradio.app/getting-started-with-the-python-client/

parrot/lib/python3.10/site-packages/numpy/lib/tests/data/py2-objarr.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:178732502fbf4c1f504852c0a3673b738e2b0ba35460882b7c6c7d3ea58f48a9
+size 258

parrot/lib/python3.10/site-packages/numpy/lib/tests/data/py2-objarr.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c68d771c14f415b159daabd9cf42d61836f74ae40049269787baca7d57098f1e
+size 366

parrot/lib/python3.10/site-packages/numpy/random/tests/data/mt19937-testset-2.csv

@@ -0,0 +1,1001 @@
+seed, 0x0
+0, 0x7ab4ea94
+1, 0x9b561119
+2, 0x4957d02e
+3, 0x7dd3fdc2
+4, 0x5affe54
+5, 0x5a01741c
+6, 0x8b9e8c1f
+7, 0xda5bf11a
+8, 0x509226
+9, 0x64e2ea17
+10, 0x82c6dab5
+11, 0xe4302515
+12, 0x8198b873
+13, 0xc3ec9a82
+14, 0x829dff28
+15, 0x5278e44f
+16, 0x994a7d2c
+17, 0xf1c89398
+18, 0xaf2fddec
+19, 0x22abc6ee
+20, 0x963dbd43
+21, 0xc29edffb
+22, 0x41c1ce07
+23, 0x9c90034d
+24, 0x1f17a796
+25, 0x3833caa8
+26, 0xb8795528
+27, 0xebc595a2
+28, 0xf8f5b5dd
+29, 0xc2881f72
+30, 0x18e5d3f0
+31, 0x9b19ac7a
+32, 0xb9992436
+33, 0xc00052b3
+34, 0xb63f4475
+35, 0x962642d9
+36, 0x63506c10
+37, 0x2be6b127
+38, 0x569bdbc6
+39, 0x7f185e01
+40, 0xebb55f53
+41, 0x1c30198c
+42, 0x7c8d75c6
+43, 0xd3f2186b
+44, 0xaca5b9b1
+45, 0xbc49ff45
+46, 0xc4a802af
+47, 0x2cecd86f
+48, 0x8e0da529
+49, 0x1f22b00e
+50, 0x4559ea80
+51, 0x60f587d8
+52, 0x7c7460e9
+53, 0x67be0a4a
+54, 0x987a0183
+55, 0x7bd30f1
+56, 0xab18c4ac
+57, 0xffdbfb64
+58, 0x9ea917f9
+59, 0x1239dab7
+60, 0x38efabeb
+61, 0x5da91888
+62, 0x8f49ed62
+63, 0x83f60b1e
+64, 0x5950a3fc
+65, 0xd8911104
+66, 0x19e8859e
+67, 0x1a4d89ec
+68, 0x968ca180
+69, 0x9e1b6da3
+70, 0x3d99c2c
+71, 0x55f76289
+72, 0x8fa28b9e
+73, 0x9fe01d33
+74, 0xdade4e38
+75, 0x1ea04290
+76, 0xa7263313
+77, 0xaafc762e
+78, 0x460476d6
+79, 0x31226e12
+80, 0x451d3f05
+81, 0xd0d2764b
+82, 0xd06e1ab3
+83, 0x1394e3f4
+84, 0x2fc04ea3
+85, 0x5b8401c
+86, 0xebd6c929
+87, 0xe881687c
+88, 0x94bdd66a
+89, 0xabf85983
+90, 0x223ad12d
+91, 0x2aaeeaa3
+92, 0x1f704934
+93, 0x2db2efb6
+94, 0xf49b8dfb
+95, 0x5bdbbb9d
+96, 0xba0cd0db
+97, 0x4ec4674e
+98, 0xad0129e
+99, 0x7a66129b
+100, 0x50d12c5e
+101, 0x85b1d335
+102, 0x3efda58a
+103, 0xecd886fb
+104, 0x8ecadd3d
+105, 0x60ebac0f
+106, 0x5e10fe79
+107, 0xa84f7e5d
+108, 0x43931288
+109, 0xfacf448
+110, 0x4ee01997
+111, 0xcdc0a651
+112, 0x33c87037
+113, 0x8b50fc03
+114, 0xf52aad34
+115, 0xda6cd856
+116, 0x7585bea0
+117, 0xe947c762
+118, 0x4ddff5d8
+119, 0xe0e79b3b
+120, 0xb804cf09
+121, 0x84765c44
+122, 0x3ff666b4
+123, 0xe31621ad
+124, 0x816f2236
+125, 0x228176bc
+126, 0xfdc14904
+127, 0x635f5077
+128, 0x6981a817
+129, 0xfd9a0300
+130, 0xd3fa8a24
+131, 0xd67c1a77
+132, 0x903fe97a
+133, 0xf7c4a4d5
+134, 0x109f2058
+135, 0x48ab87fe
+136, 0xfd6f1928
+137, 0x707e9452
+138, 0xf327db9e
+139, 0x7b80d76d
+140, 0xfb6ba193
+141, 0x454a1ad0
+142, 0xe20b51e
+143, 0xb774d085
+144, 0x6b1ed574
+145, 0xb1e77de4
+146, 0xe2a83b37
+147, 0x33d3176f
+148, 0x2f0ca0fc
+149, 0x17f51e2
+150, 0x7c1fbf55
+151, 0xf09e9cd0
+152, 0xe3d9bacd
+153, 0x4244db0a
+154, 0x876c09fc
+155, 0x9db4fc2f
+156, 0xd3771d60
+157, 0x25fc6a75
+158, 0xb309915c
+159, 0xc50ee027
+160, 0xaa5b7b38
+161, 0x4c650ded
+162, 0x1acb2879
+163, 0x50db5887
+164, 0x90054847
+165, 0xfef23e5b
+166, 0x2dd7b7d5
+167, 0x990b8c2e
+168, 0x6001a601
+169, 0xb5d314c4
+170, 0xfbfb7bf9
+171, 0x1aba997d
+172, 0x814e7304
+173, 0x989d956a
+174, 0x86d5a29c
+175, 0x70a9fa08
+176, 0xc4ccba87
+177, 0x7e9cb366
+178, 0xee18eb0a
+179, 0x44f5be58
+180, 0x91d4af2d
+181, 0x5ab6e593
+182, 0x9fd6bb4d
+183, 0x85894ce
+184, 0x728a2401
+185, 0xf006f6d4
+186, 0xd782741e
+187, 0x842cd5bd
+188, 0xfb5883aa
+189, 0x7e5a471
+190, 0x83ff6965
+191, 0xc9675c6b
+192, 0xb6ced3c7
+193, 0x3de6425b
+194, 0x25e14db4
+195, 0x69ca3dec
+196, 0x81342d13
+197, 0xd7cd8417
+198, 0x88d15e69
+199, 0xefba17c9
+200, 0x43d595e6
+201, 0x89d4cf25
+202, 0x7cae9b9b
+203, 0x2242c621
+204, 0x27fc3598
+205, 0x467b1d84
+206, 0xe84d4622
+207, 0xa26bf980
+208, 0x80411010
+209, 0xe2c2bfea
+210, 0xbc6ca25a
+211, 0x3ddb592a
+212, 0xdd46eb9e
+213, 0xdfe8f657
+214, 0x2cedc974
+215, 0xf0dc546b
+216, 0xd46be68f
+217, 0x26d8a5aa
+218, 0x76e96ba3
+219, 0x7d5b5353
+220, 0xf532237c
+221, 0x6478b79
+222, 0x9b81a5e5
+223, 0x5fc68e5c
+224, 0x68436e70
+225, 0x2a0043f9
+226, 0x108d523c
+227, 0x7a4c32a3
+228, 0x9c84c742
+229, 0x6f813dae
+230, 0xfcc5bbcc
+231, 0x215b6f3a
+232, 0x84cb321d
+233, 0x7913a248
+234, 0xb1e6b585
+235, 0x49376b31
+236, 0x1dc896b0
+237, 0x347051ad
+238, 0x5524c042
+239, 0xda0eef9d
+240, 0xf2e73342
+241, 0xbeee2f9d
+242, 0x7c702874
+243, 0x9eb3bd34
+244, 0x97b09700
+245, 0xcdbab1d4
+246, 0x4a2f6ed1
+247, 0x2047bda5
+248, 0x3ecc7005
+249, 0x8d0d5e67
+250, 0x40876fb5
+251, 0xb5fd2187
+252, 0xe915d8af
+253, 0x9a2351c7
+254, 0xccc658ae
+255, 0xebb1eddc
+256, 0xc4a83671
+257, 0xffb2548f
+258, 0xe4fe387a
+259, 0x477aaab4
+260, 0x8475a4e4
+261, 0xf8823e46
+262, 0xe4130f71
+263, 0xbdb54482
+264, 0x98fe0462
+265, 0xf36b27b8
+266, 0xed7733da
+267, 0x5f428afc
+268, 0x43a3a21a
+269, 0xf8370b55
+270, 0xfade1de1
+271, 0xd9a038ea
+272, 0x3c69af23
+273, 0x24df7dd0
+274, 0xf66d9353
+275, 0x71d811be
+276, 0xcc4d024b
+277, 0xb8c30bf0
+278, 0x4198509d
+279, 0x8b37ba36
+280, 0xa41ae29a
+281, 0x8cf7799e
+282, 0x5cd0136a
+283, 0xa11324ef
+284, 0x2f8b6d4b
+285, 0x3657cf17
+286, 0x35b6873f
+287, 0xee6e5bd7
+288, 0xbeeaa98
+289, 0x9ad3c581
+290, 0xe2376c3f
+291, 0x738027cc
+292, 0x536ac839
+293, 0xf066227
+294, 0x6c9cb0f9
+295, 0x84082ae6
+296, 0xab38ae9d
+297, 0x493eade9
+298, 0xcb630b3a
+299, 0x64d44250
+300, 0xe5efb557
+301, 0xea2424d9
+302, 0x11a690ba
+303, 0x30a48ae4
+304, 0x58987e53
+305, 0x94ec6076
+306, 0x5d3308fa
+307, 0xf1635ebb
+308, 0x56a5ab90
+309, 0x2b2f2ee4
+310, 0x6f9e6483
+311, 0x8b93e327
+312, 0xa7ce140b
+313, 0x4c8aa42
+314, 0x7657bb3f
+315, 0xf250fd75
+316, 0x1edfcb0f
+317, 0xdb42ace3
+318, 0xf8147e16
+319, 0xd1992bd
+320, 0x64bb14d1
+321, 0x423e724d
+322, 0x7b172f7c
+323, 0x17171696
+324, 0x4acaf83b
+325, 0x7a83527e
+326, 0xfc980c60
+327, 0xc8b56bb
+328, 0x2453f77f
+329, 0x85ad1bf9
+330, 0x62a85dfe
+331, 0x48238c4d
+332, 0xbb3ec1eb
+333, 0x4c1c039c
+334, 0x1f37f571
+335, 0x98aecb63
+336, 0xc3b3ddd6
+337, 0xd22dad4
+338, 0xe49671a3
+339, 0xe3baf945
+340, 0xb9e21680
+341, 0xda562856
+342, 0xe8b88ce4
+343, 0x86f88de2
+344, 0x986faf76
+345, 0x6f0025c3
+346, 0x3fe21234
+347, 0xd8d3f729
+348, 0xc2d11c6f
+349, 0xd4f9e8f
+350, 0xf61a0aa
+351, 0xc48bb313
+352, 0xe944e940
+353, 0xf1801b2e
+354, 0x253590be
+355, 0x981f069d
+356, 0x891454d8
+357, 0xa4f824ad
+358, 0x6dd2cc48
+359, 0x3018827e
+360, 0x3fb329e6
+361, 0x65276517
+362, 0x8d2c0dd2
+363, 0xc965b48e
+364, 0x85d14d90
+365, 0x5a51623c
+366, 0xa9573d6a
+367, 0x82d00edf
+368, 0x5ed7ce07
+369, 0x1d946abc
+370, 0x24fa567b
+371, 0x83ef5ecc
+372, 0x9001724a
+373, 0xc4fe48f3
+374, 0x1e07c25c
+375, 0xf4d5e65e
+376, 0xb734f6e9
+377, 0x327a2df8
+378, 0x766d59b7
+379, 0x625e6b61
+380, 0xe82f32d7
+381, 0x1566c638
+382, 0x2e815871
+383, 0x606514aa
+384, 0x36b7386e
+385, 0xcaa8ce08
+386, 0xb453fe9c
+387, 0x48574e23
+388, 0x71f0da06
+389, 0xa8a79463
+390, 0x6b590210
+391, 0x86e989db
+392, 0x42899f4f
+393, 0x7a654ef9
+394, 0x4c4fe932
+395, 0x77b2fd10
+396, 0xb6b4565c
+397, 0xa2e537a3
+398, 0xef5a3dca
+399, 0x41235ea8
+400, 0x95c90541
+401, 0x50ad32c4
+402, 0xc1b8e0a4
+403, 0x498e9aab
+404, 0xffc965f1
+405, 0x72633485
+406, 0x3a731aef
+407, 0x7cfddd0b
+408, 0xb04d4129
+409, 0x184fc28e
+410, 0x424369b0
+411, 0xf9ae13a1
+412, 0xaf357c8d
+413, 0x7a19228e
+414, 0xb46de2a8
+415, 0xeff2ac76
+416, 0xa6c9357b
+417, 0x614f19c1
+418, 0x8ee1a53f
+419, 0xbe1257b1
+420, 0xf72651fe
+421, 0xd347c298
+422, 0x96dd2f23
+423, 0x5bb1d63e
+424, 0x32e10887
+425, 0x36a144da
+426, 0x9d70e791
+427, 0x5e535a25
+428, 0x214253da
+429, 0x2e43dd40
+430, 0xfc0413f4
+431, 0x1f5ea409
+432, 0x1754c126
+433, 0xcdbeebbe
+434, 0x1fb44a14
+435, 0xaec7926
+436, 0xb9d9a1e
+437, 0x9e4a6577
+438, 0x8b1f04c5
+439, 0x19854e8a
+440, 0x531080cd
+441, 0xc0cbd73
+442, 0x20399d77
+443, 0x7d8e9ed5
+444, 0x66177598
+445, 0x4d18a5c2
+446, 0xe08ebf58
+447, 0xb1f9c87b
+448, 0x66bedb10
+449, 0x26670d21
+450, 0x7a7892da
+451, 0x69b69d86
+452, 0xd04f1d1c
+453, 0xaf469625
+454, 0x7946b813
+455, 0x1ee596bd
+456, 0x7f365d85
+457, 0x795b662b
+458, 0x194ad02d
+459, 0x5a9649b5
+460, 0x6085e278
+461, 0x2cf54550
+462, 0x9c77ea0b
+463, 0x3c6ff8b
+464, 0x2141cd34
+465, 0xb90bc671
+466, 0x35037c4b
+467, 0xd04c0d76
+468, 0xc75bff8
+469, 0x8f52003b
+470, 0xfad3d031
+471, 0x667024bc
+472, 0xcb04ea36
+473, 0x3e03d587
+474, 0x2644d3a0
+475, 0xa8fe99ba
+476, 0x2b9a55fc
+477, 0x45c4d44a
+478, 0xd059881
+479, 0xe07fcd20
+480, 0x4e22046c
+481, 0x7c2cbf81
+482, 0xbf7f23de
+483, 0x69d924c3
+484, 0xe53cd01
+485, 0x3879017c
+486, 0xa590e558
+487, 0x263bc076
+488, 0x245465b1
+489, 0x449212c6
+490, 0x249dcb29
+491, 0x703d42d7
+492, 0x140eb9ec
+493, 0xc86c5741
+494, 0x7992aa5b
+495, 0xb8b76a91
+496, 0x771dac3d
+497, 0x4ecd81e3
+498, 0xe5ac30b3
+499, 0xf4d7a5a6
+500, 0xac24b97
+501, 0x63494d78
+502, 0x627ffa89
+503, 0xfa4f330
+504, 0x8098a1aa
+505, 0xcc0c61dc
+506, 0x34749fa0
+507, 0x7f217822
+508, 0x418d6f15
+509, 0xa4b6e51e
+510, 0x1036de68
+511, 0x1436986e
+512, 0x44df961d
+513, 0x368e4651
+514, 0x6a9e5d8c
+515, 0x27d1597e
+516, 0xa1926c62
+517, 0x8d1f2b55
+518, 0x5797eb42
+519, 0xa90f9e81
+520, 0x57547b10
+521, 0xdbbcca8e
+522, 0x9edd2d86
+523, 0xbb0a7527
+524, 0x7662380c
+525, 0xe7c98590
+526, 0x950fbf3f
+527, 0xdc2b76b3
+528, 0x8a945102
+529, 0x3f0a1a85
+530, 0xeb215834
+531, 0xc59f2802
+532, 0xe2a4610
+533, 0x8b5a8665
+534, 0x8b2d9933
+535, 0x40a4f0bc
+536, 0xaab5bc67
+537, 0x1442a69e
+538, 0xdf531193
+539, 0x698d3db4
+540, 0x2d40324e
+541, 0x1a25feb2
+542, 0xe8cc898f
+543, 0xf12e98f5
+544, 0xc03ad34c
+545, 0xf62fceff
+546, 0xdd827e1e
+547, 0x7d8ccb3b
+548, 0xab2d6bc1
+549, 0xc323a124
+550, 0x8184a19a
+551, 0xc3c4e934
+552, 0x5487424d
+553, 0xd6a81a44
+554, 0x90a8689d
+555, 0xe69c4c67
+556, 0xbdae02dd
+557, 0x72a18a79
+558, 0x2a88e907
+559, 0x31cf4b5d
+560, 0xb157772f
+561, 0x206ba601
+562, 0x18529232
+563, 0x7dac90d8
+564, 0x3a5f8a09
+565, 0x9f4b64a3
+566, 0xae373af9
+567, 0x1d79447c
+568, 0x2a23684b
+569, 0x41fb7ba4
+570, 0x55e4bb9e
+571, 0xd7619d3e
+572, 0xc04e4dd8
+573, 0x8418d516
+574, 0x2b2ca585
+575, 0xfa8eedf
+576, 0x5bafd977
+577, 0x31974fb0
+578, 0x9eb6697b
+579, 0xc8be22f5
+580, 0x173b126a
+581, 0x8809becf
+582, 0x3e41efe1
+583, 0x3d6cbbb8
+584, 0x278c81d8
+585, 0xa6f08434
+586, 0xa0e6601d
+587, 0x2fccd88d
+588, 0x3cbc8beb
+589, 0x5f65d864
+590, 0xa1ff8ddf
+591, 0x609dcb7c
+592, 0x4a4e1663
+593, 0xeae5531
+594, 0x962a7c85
+595, 0x1e110607
+596, 0x8c5db5d0
+597, 0xc7f2337e
+598, 0xc94fcc9c
+599, 0xe7f62629
+600, 0x6c9aa9f8
+601, 0x2e27fe0e
+602, 0x4d0dae12
+603, 0x9eecf588
+604, 0x977ba3f2
+605, 0xed0a51af
+606, 0x3f3ec633
+607, 0xc174b2ec
+608, 0x590be8a9
+609, 0x4f630d18
+610, 0xf579e989
+611, 0xe2a55584
+612, 0xee11edcd
+613, 0x150a4833
+614, 0xc0a0535c
+615, 0xb5e00993
+616, 0xb6435700
+617, 0xa98dbff
+618, 0x315716af
+619, 0x94395776
+620, 0x6cbd48d9
+621, 0xab17f8fc
+622, 0xa794ffb7
+623, 0x6b55e231
+624, 0x89ff5783
+625, 0x431dcb26
+626, 0x270f9bf8
+627, 0x2af1b8d0
+628, 0x881745ed
+629, 0x17e1be4e
+630, 0x132a0ec4
+631, 0x5712df17
+632, 0x2dfb3334
+633, 0xf5a35519
+634, 0xcafbdac6
+635, 0x73b6189d
+636, 0x10107cac
+637, 0x18c1045e
+638, 0xbc19bbad
+639, 0x8b4f05ac
+640, 0x5830d038
+641, 0x468cd98a
+642, 0x5b83a201
+643, 0xf0ccdd9c
+644, 0xcb20c4bd
+645, 0x1ff186c9
+646, 0xcdddb47f
+647, 0x5c65ce6
+648, 0xb748c580
+649, 0x23b6f262
+650, 0xe2ba8e5c
+651, 0x9a164a03
+652, 0x62d3322e
+653, 0x918d8b43
+654, 0x45c8b49d
+655, 0xce172c6e
+656, 0x23febc6
+657, 0x84fdc5b7
+658, 0xe7d1fd82
+659, 0xf0ddf3a6
+660, 0x87050436
+661, 0x13d46375
+662, 0x5b191c78
+663, 0x2cbd99c0
+664, 0x7686c7f
+665, 0xcff56c84
+666, 0x7f9b4486
+667, 0xefc997fe
+668, 0x984d4588
+669, 0xfa44f36a
+670, 0x7a5276c1
+671, 0xcfde6176
+672, 0xcacf7b1d
+673, 0xcffae9a7
+674, 0xe98848d5
+675, 0xd4346001
+676, 0xa2196cac
+677, 0x217f07dc
+678, 0x42d5bef
+679, 0x6f2e8838
+680, 0x4677a24
+681, 0x4ad9cd54
+682, 0x43df42af
+683, 0x2dde417
+684, 0xaef5acb1
+685, 0xf377f4b3
+686, 0x7d870d40
+687, 0xe53df1c2
+688, 0xaeb5be50
+689, 0x7c92eac0
+690, 0x4f00838c
+691, 0x91e05e84
+692, 0x23856c80
+693, 0xc4266fa6
+694, 0x912fddb
+695, 0x34d42d22
+696, 0x6c02ffa
+697, 0xe47d093
+698, 0x183c55b3
+699, 0xc161d142
+700, 0x3d43ff5f
+701, 0xc944a36
+702, 0x27bb9fc6
+703, 0x75c91080
+704, 0x2460d0dc
+705, 0xd2174558
+706, 0x68062dbf
+707, 0x778e5c6e
+708, 0xa4dc9a
+709, 0x7a191e69
+710, 0xc084b2ba
+711, 0xbb391d2
+712, 0x88849be
+713, 0x69c02714
+714, 0x69d4a389
+715, 0x8f51854d
+716, 0xaf10bb82
+717, 0x4d5d1c77
+718, 0x53b53109
+719, 0xa0a92aa0
+720, 0x83ecb757
+721, 0x5325752a
+722, 0x114e466e
+723, 0x4b3f2780
+724, 0xa7a6a39c
+725, 0x5e723357
+726, 0xa6b8be9b
+727, 0x157c32ff
+728, 0x8b898012
+729, 0xd7ff2b1e
+730, 0x69cd8444
+731, 0x6ad8030c
+732, 0xa08a49ec
+733, 0xfbc055d3
+734, 0xedf17e46
+735, 0xc9526200
+736, 0x3849b88a
+737, 0x2746860b
+738, 0xae13d0c1
+739, 0x4f15154f
+740, 0xd65c3975
+741, 0x6a377278
+742, 0x54d501f7
+743, 0x81a054ea
+744, 0x143592ba
+745, 0x97714ad6
+746, 0x4f9926d9
+747, 0x4f7ac56d
+748, 0xe87ca939
+749, 0x58b76f6f
+750, 0x60901ad8
+751, 0x3e401bb6
+752, 0xa058468e
+753, 0xc0bb14f6
+754, 0x2cb8f02a
+755, 0x7c2cf756
+756, 0x34c31de5
+757, 0x9b243e83
+758, 0xa5c85ab4
+759, 0x2741e3b3
+760, 0x1249000e
+761, 0x3fc4e72b
+762, 0xa3e038a2
+763, 0x952dd92c
+764, 0x2b821966
+765, 0xfa81b365
+766, 0x530919b9
+767, 0x4486d66f
+768, 0xccf4f3c1
+769, 0xa8bddd1d
+770, 0xcc295eb9
+771, 0xfccbe42f
+772, 0x38bacd8d
+773, 0x2261854f
+774, 0x56068c62
+775, 0x9bdaeb8
+776, 0x555fa5b6
+777, 0x20fe615e
+778, 0x49fb23d3
+779, 0xd093bad6
+780, 0x54919e86
+781, 0x7373eb24
+782, 0xfbaa7a98
+783, 0x5f62fb39
+784, 0xe03bc9ec
+785, 0xa5074d41
+786, 0xa1cefb1
+787, 0x13912d74
+788, 0xf6421b8
+789, 0xfcb48812
+790, 0x8f1db50b
+791, 0xc1654b87
+792, 0x948b43c2
+793, 0xf503ef77
+794, 0x117d891d
+795, 0x5493ffa
+796, 0x171313b1
+797, 0xa4b62e1e
+798, 0x77454ea6
+799, 0xbea0aff0
+800, 0x13c36389
+801, 0xe3b60bac
+802, 0xa176bed3
+803, 0x2863d428
+804, 0xe2314f46
+805, 0xa85cd3d4
+806, 0x7866e57
+807, 0x8f03f5bc
+808, 0x239ae
+809, 0x46f279fb
+810, 0xcca00559
+811, 0xaa07a104
+812, 0x89123d08
+813, 0x2e6856ba
+814, 0x43a9780d
+815, 0x676cff25
+816, 0x6744b87d
+817, 0xee260d4f
+818, 0xb98d8b77
+819, 0x9b0ca455
+820, 0x659f6fe
+821, 0x28d20d1c
+822, 0x601f2657
+823, 0xdec3073e
+824, 0x61263863
+825, 0x1a13435a
+826, 0x27497d1e
+827, 0x17a8458e
+828, 0xdddc407d
+829, 0x4bb2e8ac
+830, 0x16b2aedb
+831, 0x77ccd696
+832, 0x9d108fcd
+833, 0x25ad233e
+834, 0xaa9bc370
+835, 0xa873ab50
+836, 0xaf19c9d9
+837, 0x696e1e6b
+838, 0x1fdc4bf4
+839, 0x4c2ebc81
+840, 0xde4929ed
+841, 0xf4d0c10c
+842, 0xb6595b76
+843, 0x75cbb1b3
+844, 0xbcb6de49
+845, 0xe23157fd
+846, 0x5e596078
+847, 0xa69b0d29
+848, 0x2118a41
+849, 0x7088c16
+850, 0xc75e1e1
+851, 0x6a4af2d6
+852, 0xf19c6521
+853, 0xaff7b3b1
+854, 0x615295c7
+855, 0xbda3a8d7
+856, 0x5b5ca72e
+857, 0xdad9d80f
+858, 0xfa81c084
+859, 0xf4703fa
+860, 0x3ca54540
+861, 0xa8961d51
+862, 0x53d1ecc2
+863, 0x808d83b6
+864, 0x68e8c48e
+865, 0x89be2039
+866, 0x9088ea11
+867, 0xb8665d12
+868, 0x91272f9
+869, 0x53dddff2
+870, 0xb7a54ab
+871, 0xd2b645ca
+872, 0x99fb8590
+873, 0x5315c8e
+874, 0x2a913806
+875, 0x7f15eb2b
+876, 0xa7f1cc5d
+877, 0xbb2ee836
+878, 0xd9fafd60
+879, 0x17448d6f
+880, 0x999ec436
+881, 0x482ec606
+882, 0x9b403c0e
+883, 0x569eb51b
+884, 0xb275d1a6
+885, 0xadd29c31
+886, 0xb7ebdb15
+887, 0xdfef3662
+888, 0x51aba6db
+889, 0x6d41946d
+890, 0x77bf8896
+891, 0xcafa6fab
+892, 0x976ab40f
+893, 0x49a6d86b
+894, 0x56639e55
+895, 0x9945b996
+896, 0x81459b50
+897, 0xbce97542
+898, 0xe397c9c9
+899, 0x247a5955
+900, 0xb72b1573
+901, 0x86306f86
+902, 0x34f65dc5
+903, 0x909360c0
+904, 0xf3f696ef
+905, 0xcb9faae5
+906, 0x93daecd9
+907, 0xde1af7af
+908, 0x43a1f2d
+909, 0x6d75cde5
+910, 0x9e412b6
+911, 0x5673fed
+912, 0x16bb511a
+913, 0x35ef4cca
+914, 0x4e615aca
+915, 0x5cdaf47a
+916, 0x26676047
+917, 0x8c199325
+918, 0x2adf0cb9
+919, 0x84f2e6fd
+920, 0x5e627f64
+921, 0xb7cee354
+922, 0x542ab4a6
+923, 0xe59cd83b
+924, 0x89cc3f10
+925, 0x92b0f5f
+926, 0xc1328370
+927, 0x8208d9f7
+928, 0x68eb00cf
+929, 0xfadd4ac4
+930, 0x2517784f
+931, 0x4042b99
+932, 0x75ce0230
+933, 0x97c5a1b4
+934, 0x1a97f709
+935, 0x4c62781e
+936, 0xf530a83
+937, 0x75776413
+938, 0x321c7240
+939, 0x6afe4e36
+940, 0xad00a2b4
+941, 0xbc05477d
+942, 0xb0911e80
+943, 0x9935b87d
+944, 0xd535eec5
+945, 0x149af45e
+946, 0x786934b0
+947, 0xbc13cdac
+948, 0x208bfa2e
+949, 0xcf4b39cc
+950, 0x6ac6c172
+951, 0xbfa9a37
+952, 0x42d28db6
+953, 0x2bf1ea63
+954, 0xbed6e677
+955, 0x50325d27
+956, 0xa79d3b8b
+957, 0x52448bb1
+958, 0xefaad1bd
+959, 0x833a2e54
+960, 0xd9de549a
+961, 0x9f59672f
+962, 0x9d5f5f16
+963, 0x1c914489
+964, 0xc08fa058
+965, 0xb188698b
+966, 0xdc4672b5
+967, 0x594f720e
+968, 0x56ed428f
+969, 0x9b0898af
+970, 0x8a64d3d5
+971, 0x773308d6
+972, 0x84d62098
+973, 0x46da7cf9
+974, 0x1114eae7
+975, 0xf9f2a092
+976, 0x5363a28
+977, 0xf2db7b3a
+978, 0x102c71a9
+979, 0xe8e76aaf
+980, 0x77a97b3b
+981, 0x77b090d
+982, 0x1099620e
+983, 0xa6daaae6
+984, 0x86ff4713
+985, 0xc0ef85b8
+986, 0xf621d409
+987, 0xfd1561e2
+988, 0x4bcc687d
+989, 0x596f760
+990, 0x7c8819f9
+991, 0x8cb865b8
+992, 0xadea115a
+993, 0x56609348
+994, 0xb321ac14
+995, 0x1bac7db2
+996, 0x5fe6ee2
+997, 0xe9bfe072
+998, 0x15549e74
+999, 0xad8c191b

parrot/lib/python3.10/site-packages/numpy/random/tests/data/pcg64-testset-2.csv

@@ -0,0 +1,1001 @@
+seed, 0x0
+0, 0xa30febcfd9c2825f
+1, 0x4510bdf882d9d721
+2, 0xa7d3da94ecde8b8
+3, 0x43b27b61342f01d
+4, 0xd0327a782cde513b
+5, 0xe9aa5979a6401c4e
+6, 0x9b4c7b7180edb27f
+7, 0xbac0495ff8829a45
+8, 0x8b2b01e7a1dc7fbf
+9, 0xef60e8078f56bfed
+10, 0xd0dbc74d4700374c
+11, 0xb37868abbe90b0
+12, 0xdb7ed8bf64e6f5f0
+13, 0x89910738de7951f
+14, 0xbacab307c3cfd379
+15, 0x2cf7c449d8b927a6
+16, 0xdcf94b3a16db7f0e
+17, 0x8a9d33d905a8792e
+18, 0x4cb9eb2014951238
+19, 0x6c353acf7b26d6f1
+20, 0x73ff53d673aa30c
+21, 0x1fd10760015eca68
+22, 0xabae0aa9021eeba8
+23, 0xa5ae363a868ee2bb
+24, 0x9d89e0f041de6631
+25, 0x6238b133c3991a65
+26, 0xff49267d75fef51a
+27, 0xfb180656ce13c53f
+28, 0xaf7fadf36128712d
+29, 0xa6847fc6f339c63e
+30, 0xb03e0b80d71ea5bc
+31, 0x63905abcb43969af
+32, 0x2295af3ee00a3bba
+33, 0xb8b375b994330415
+34, 0x867d9ef1d8716a3b
+35, 0x4f6c02f5601b4e18
+36, 0x7c5fb4c16c470d18
+37, 0xe3b57986b804b343
+38, 0xef1d79d212aca692
+39, 0x5b98774c8806209c
+40, 0x924fc76bac38a5d1
+41, 0x5266084c412ddeed
+42, 0x98240bf9b831d6a3
+43, 0x5681599e81219442
+44, 0x6441248fc2ba92bc
+45, 0xe3e9051a540349ea
+46, 0x3a2700034390baa3
+47, 0x9f893155b6d402bc
+48, 0x158207910c6d8aef
+49, 0xd5282ab7608c2cbc
+50, 0xc97f4651669dee4f
+51, 0x3d4750d95103ed60
+52, 0xe0614542caac1f04
+53, 0xefe5092144cfc6c
+54, 0x560bc486abd7e9ae
+55, 0x2678b71392daa4b8
+56, 0x734970d3dc2ba416
+57, 0xcbdbe849e51e4aaf
+58, 0x3b0b5e28b491556c
+59, 0xd51449ac45abd88
+60, 0x6790b59991f1b7ab
+61, 0x32d1c039ff2415bc
+62, 0x173b9772f24f72e0
+63, 0x9490a9ca9f883b1b
+64, 0x4c775989e6214222
+65, 0xac07db37e6ee6114
+66, 0x331371b2e3f10aee
+67, 0xf12e5326c21c28e4
+68, 0x5d77dc280c70d614
+69, 0x1b01bd17a2f281ec
+70, 0xa10d3b5882938487
+71, 0xed5a0033c394ae8f
+72, 0x70bc8ea568ea44b4
+73, 0xf4600ae77965e730
+74, 0x7ff92c0b321ce233
+75, 0x6cdbc87d0cc1d670
+76, 0x9ec64f0cf2000eb1
+77, 0xfebea50259800f68
+78, 0xf2edf9019a8fd343
+79, 0x75c584ac042e5468
+80, 0xc1fa8481d5bf9a1d
+81, 0x7f57180168514ac2
+82, 0x878100716b94f81e
+83, 0xc929406e3af17fd2
+84, 0x6a26e2c013e4bf4d
+85, 0xbc071d8848280955
+86, 0xb60d75abbfd1bdac
+87, 0xee9b76afeca9fa69
+88, 0x1d6c399d2f452810
+89, 0xbaa0bc1621e25c83
+90, 0xed6ba792f8671ba5
+91, 0xf7ca02c2ab11d8d7
+92, 0x3c3cadadf0b21e3
+93, 0xdd1784571e864e9c
+94, 0xfb2f992015157509
+95, 0xf50bb9f0d3ced743
+96, 0x261565f75c3e185f
+97, 0xf8fe33b284513e60
+98, 0xe3d2d10b5e024664
+99, 0xd28717566242cf35
+100, 0x7ae07d133ac5b789
+101, 0x3b7ccaaa53ac338e
+102, 0xcd480bace4871650
+103, 0xec6c78f923c080e9
+104, 0x44211d0ff8919d59
+105, 0x89f79af76d2a45fe
+106, 0x71583fd8a837548b
+107, 0xee57269c261511f5
+108, 0xa5ee8f3b128c5d1
+109, 0xbb64c20ed0765a17
+110, 0x9d4790ab2eeaf7e4
+111, 0x742f3db806d9e98
+112, 0xb81ec97aed6a0d1b
+113, 0x41808b34f6a8a23
+114, 0xc20913af175dfd4d
+115, 0x834427db263b22bb
+116, 0xedd9c632e611828a
+117, 0x10eac8524496f571
+118, 0xd76091b97eb00ab7
+119, 0x111298ae9fe95666
+120, 0x5824b2e2a6719c43
+121, 0x6e280ec539e934ed
+122, 0xf74fd832df90083e
+123, 0x8fee6d0f241c2e97
+124, 0x4244f331c2f19c3c
+125, 0x3dde75a845cce97f
+126, 0xe35bb8e635a9915b
+127, 0x39d2943037f7932e
+128, 0x1fe2d134201d0970
+129, 0x49d00b63c749b804
+130, 0x960c2942cd4e4e04
+131, 0x8dd8e009dbc0435f
+132, 0xcf493495c3a055cd
+133, 0x8f7b5a1c0f9fe9cd
+134, 0x49d5f90374641a25
+135, 0x69b3932073d3524c
+136, 0xd170603e7de84ee2
+137, 0xa062ba3ed3539948
+138, 0xf5861cc5b5d56c82
+139, 0x5e914998a30c7e76
+140, 0x8d77f2ad1503c0f1
+141, 0x980b6a9e3b4181fb
+142, 0xd9299cd50694c084
+143, 0x253dc0f8f1cec4c5
+144, 0x68110fb9d1b3e695
+145, 0xe8f3120d0aabc461
+146, 0xb066e7df0dfb042
+147, 0xd29ce0f797e6b60b
+148, 0x6a569bb7ca33bd42
+149, 0xd46e08b2dc2385f8
+150, 0x28c61d11d055767
+151, 0x5d73aa3d1a2bb725
+152, 0x1421191e1c14829a
+153, 0xa711bfb6423df35e
+154, 0x461af97a86308006
+155, 0xb3e1018ff3519367
+156, 0xf19cf866a268ef2b
+157, 0x207715eac9199d1d
+158, 0xdd621c410975b78c
+159, 0xf390aea68683610
+160, 0x617a2d107a0047d9
+161, 0x6e05ac416e5bebf0
+162, 0x7d253e70506c1bed
+163, 0xf9f96f4a7dd53810
+164, 0xc693b29cb1573f73
+165, 0x4f1146b0020ea544
+166, 0x45140608fbd40579
+167, 0xdcf57219828ce6be
+168, 0xe19d58cca37b5b32
+169, 0x82bda95b2a161235
+170, 0x5823c3d8a2b6c9ba
+171, 0xfeb2e74092fdf89a
+172, 0x50e1ad1abc8f869d
+173, 0x2ec63d0c105eb8da
+174, 0xe14e1c4845a3264a
+175, 0xcff53670455eb6aa
+176, 0xaafaccd24619fa3e
+177, 0xf55a988486e2422a
+178, 0xecfba16a90ff4d04
+179, 0xbf8d36c2f644757a
+180, 0xdc56ed75a0dd6249
+181, 0x3f45023eff17c3bb
+182, 0x2428bbfe90023fab
+183, 0xab892c611adcb70c
+184, 0xb6f13d8c0c2b9d74
+185, 0x2ac3fb11d224f2a8
+186, 0x65433dcfae2d9351
+187, 0xe906859ae4b45f82
+188, 0x8fb7f5f093d76a3b
+189, 0x940dd290b5e88d1a
+190, 0x31b27d21bef116e7
+191, 0x86a964e2c83b5296
+192, 0x85ffd17bc079a9e8
+193, 0x16c47c724e7ab7f1
+194, 0xfb6098a9867e7d7f
+195, 0x9246fb69092c6cb2
+196, 0x1a4033572760f32
+197, 0xc5cc568a8b273b84
+198, 0xfa6f9f2fbdd44abc
+199, 0x9701b8e087718ba3
+200, 0x51d6a7dcf73f8f3a
+201, 0x30008172cc6a972d
+202, 0xac2ab49a5ca6ac81
+203, 0x31f28ef79461e54c
+204, 0x93e35a8da8cc6132
+205, 0x9a2c58beeba3d5b9
+206, 0xf6615c1de266ac39
+207, 0x127ff9f8166b766b
+208, 0x7ffe380e80a69556
+209, 0xbe7d2c228e1542f7
+210, 0x2d5ebb4e50ba1746
+211, 0x63585761ae1bf684
+212, 0x1019eb5cee022fea
+213, 0xb9d3540ab58da30d
+214, 0x1677f4cb45620eb9
+215, 0x6524baee51783822
+216, 0xdf9f2ddcfabb0adc
+217, 0x78e8acc43b287935
+218, 0xe9a1974e999222b5
+219, 0xc41324ec2291e780
+220, 0xea52abc9ecdcbc9f
+221, 0x209d7bcd46ec6b04
+222, 0x12d504c09803db2e
+223, 0x1200e6bf21475d81
+224, 0xde6d3c2b35fd2cfc
+225, 0xa2526900ac33bd3c
+226, 0x7f1f5290fc432bc5
+227, 0x29ddfb380a3d69c8
+228, 0xac79cb6942a2909d
+229, 0x516996685b67a92a
+230, 0xb5fc39041cb828bb
+231, 0x75d9d8ca0644a276
+232, 0x81e98b76be92a3e9
+233, 0xca27888fafe12179
+234, 0x17be2ae039925765
+235, 0x9429846c0e6d0342
+236, 0x327dfd50439815e9
+237, 0xcee20cd7bc254aeb
+238, 0x7d250389f453f29e
+239, 0xfd1b232a85c95569
+240, 0x2ed55fac80f3e9e9
+241, 0xf6886c20417a1be7
+242, 0xcd08e61f0b0fdfde
+243, 0x7b33e34da5c27bff
+244, 0xd043c4b7d5603dd5
+245, 0x9a544e4c70a3b686
+246, 0xa7b60398c381f771
+247, 0xe9e7a3487c4bd4f2
+248, 0x10b58fdfe1ff112c
+249, 0xd5c1c9748c0f4ceb
+250, 0x61be9d09159d54ff
+251, 0x5356f51e8239f510
+252, 0xfe7889d9b202ecef
+253, 0xc7fc19ca5d263d5d
+254, 0x7c4c07e61dfd9f69
+255, 0x6c315fe5015f300a
+256, 0xe0a5bc00039747b4
+257, 0x16397fdcf829ee80
+258, 0xb55aee80d16a5169
+259, 0xca0609944d007eea
+260, 0xcc982249f65a02ce
+261, 0x528161feb149c148
+262, 0xcbf08ba49b41c006
+263, 0x39af1ff0b6f14138
+264, 0x5cc036be69799aec
+265, 0x6adde125b1db21c5
+266, 0x8a99d83d6b613b67
+267, 0x1cd43fca9451f74c
+268, 0x682dbb26ecc96365
+269, 0x13b4be2ceb43e3
+270, 0xbe8fbc3b6f4f581e
+271, 0xda148a2f4bda5719
+272, 0x239106ca3319f393
+273, 0xb42b4dde641f0dd5
+274, 0xd233cfdf4cb0af74
+275, 0xfb5919d905589afc
+276, 0xd802a8860c10b66a
+277, 0x6c923e1d00e7b5bc
+278, 0xfacce1134f383b89
+279, 0xf9570abda7a6d553
+280, 0x80f0f9796a208f18
+281, 0xc0e1df5280951c57
+282, 0xe9f143f08257bbe0
+283, 0x79e4c6463123d588
+284, 0xdd2118583f2b1684
+285, 0xb399ff5f2329fa18
+286, 0x4b3e9ebae96f813c
+287, 0xc484dbf247787384
+288, 0x921865eb97603f2c
+289, 0x18063c68e257d300
+290, 0x643181f345e7fc26
+291, 0x12e0b0e8eadf9fa7
+292, 0x79e613fe73dfa354
+293, 0x6db4c59203b7217a
+294, 0x6c7a0e9ba6139eaf
+295, 0x9617c7ac4e3f6d97
+296, 0x1f68a7b4fb1b4b75
+297, 0xef0b7ab24944f466
+298, 0xaf1dee1f4be1bc89
+299, 0xd2e355c959f5fd8d
+300, 0xe594c3fb95d96efc
+301, 0x9554766ca3342906
+302, 0xa4bbdc77d12842c
+303, 0xb62400211ee489a8
+304, 0x91abadaaa3bbe67c
+305, 0xd371eeb91deb42bb
+306, 0x883bab35cbd2b6e5
+307, 0xd030c3d9411a9041
+308, 0xff3c110a858ff000
+309, 0x59bdf5ca47d0bde7
+310, 0x2bc80fa3cdba1853
+311, 0x6444ccb652662cb8
+312, 0xc0c7e256b9e90339
+313, 0x70714ea9c9d72302
+314, 0x96a0142f9d897d27
+315, 0x209a9097c5a91ef7
+316, 0xb9e33afc5171e009
+317, 0x47b37af433a58d40
+318, 0x30cc4ffbfa831d26
+319, 0xdcea4a85ff815466
+320, 0x907d5bd027f2e5cc
+321, 0x7c081f6852e04a4b
+322, 0xe61950749c1d502b
+323, 0x1604e937ee69834a
+324, 0xb2372d952dd25309
+325, 0x53f6a5b834c72577
+326, 0x2ce7a74395e0b694
+327, 0xacbf9ab4fe91f225
+328, 0x5ce1e63d3a2bb90f
+329, 0x54740da3a5ed139b
+330, 0xf194ddb39f29880b
+331, 0x3305374f5d8ec08b
+332, 0x831dd0164927ff4a
+333, 0x625baa78e4458cf
+334, 0x29d27dc0a4a71152
+335, 0xe227bae9a1401034
+336, 0xca0c209831846b2b
+337, 0x8e8cc54b08b5a411
+338, 0x38f2b4acaac27db6
+339, 0x8ec88baac814e86b
+340, 0x31c08e46b007bde
+341, 0xb686c02722794c09
+342, 0xb77cf8fc682e3907
+343, 0xa56334e7f606f4b2
+344, 0x9c80b127bddd5f4f
+345, 0x12df14834cd858bf
+346, 0x3f14762a9cf5fb9f
+347, 0x930a70941ef5779e
+348, 0x64e96c849c30c080
+349, 0xfdf53bfba1300484
+350, 0xec7a9363c21bc616
+351, 0x26e9fd6a115ecb47
+352, 0x9707a84b5bc77fbb
+353, 0xb23b2737b20d5903
+354, 0x22f4825ae80f6501
+355, 0x500644b12be6a01b
+356, 0xb746645b2af082db
+357, 0xe6af051f697892f8
+358, 0x577c724248a1cfc6
+359, 0x3d2b6a434c84eed3
+360, 0xd260f5efd7328314
+361, 0x95c16cc84bb3f55c
+362, 0x7a01b2e4e0e80ca7
+363, 0x41930c3ce70a0935
+364, 0x1299bccf39d4e110
+365, 0x494883ba1a8a87f
+366, 0x9478ecfe2d918e60
+367, 0x30ec9a5670cda8af
+368, 0xf9bc877e833e2b99
+369, 0x1b83a0acfbb4a8db
+370, 0x73bc1740c0d18880
+371, 0x65086ca9773cb3e1
+372, 0x3b78c3ccd63cff2e
+373, 0xbfae748795acfb31
+374, 0xa4c9d5d56a15ba20
+375, 0xb9cb41721e52b71e
+376, 0x1532f15d4dc47748
+377, 0x5a4d647a4b9ee632
+378, 0x8513c7c5a50898d9
+379, 0x6d3d98ccd5461b2e
+380, 0xa65e99be2fe98d6
+381, 0x31abc8855334a0e5
+382, 0xf1ed22a661dca5b8
+383, 0x299e2b63229e03be
+384, 0xda201a06687bce48
+385, 0xd27794b302142c55
+386, 0x642bd3e1c7898a9d
+387, 0x777f1ff00afa1a87
+388, 0xd2f1c84fb3877baa
+389, 0xae417583289191fd
+390, 0xd641f1d88e0e2d55
+391, 0xc1f1d98fb5d18ebf
+392, 0xb0f72aecdadce97b
+393, 0xe9b8abc764f6018a
+394, 0xd2a37cff8e890594
+395, 0x2dd70d631a528771
+396, 0xbf8ba0478c18e336
+397, 0x1630bf47f372ce0a
+398, 0x6d04ea20dc3f46b8
+399, 0x6591881bf34337f2
+400, 0x33c149c7eb5b4103
+401, 0xf01a8c9857c86748
+402, 0x184348cdfc16d215
+403, 0x141168b253d2ed7
+404, 0x52aaf012ef50a6f1
+405, 0xfda1722387e16f4c
+406, 0x43c30f57d6c038fa
+407, 0xd4a8611f5f96d214
+408, 0x2c512ce17e987f2c
+409, 0x961ce450f0fa2822
+410, 0xf55a506ec6cea9cd
+411, 0xb76d694d9c7f5ef6
+412, 0xfb029216dbd8e988
+413, 0x93162501896a0081
+414, 0xfbbbd2c5ab300f5c
+415, 0xd648b6da7387d491
+416, 0xc73b4697471d9d98
+417, 0xe37412bf1c93ee76
+418, 0xa1a96d96570e6637
+419, 0x5b3ab4f82428f65c
+420, 0x873d849b188aa36f
+421, 0x39fbee0ffc9fa9ff
+422, 0xc70d21b744d677fe
+423, 0x2b8a43c23043d209
+424, 0x93c33eaa37370d16
+425, 0x8930ac1880f2b0ef
+426, 0xac01d27707036af0
+427, 0xc2af3fee504343a0
+428, 0x1c1dae2ad5535d97
+429, 0x9ffc21804b76a480
+430, 0x69f903412cc13563
+431, 0x9d3c4e2759a0c47d
+432, 0xb1a8f894be6302b9
+433, 0x95e1fd7951479506
+434, 0xbb9e6c03cd4ae8e3
+435, 0x85206010c9b737cf
+436, 0x767e813694d6238c
+437, 0x4969af329ccbb30a
+438, 0x3aa9af1075aaea5c
+439, 0xb1ff519e8118a993
+440, 0xb21a23a3c91180fe
+441, 0x320b24582ca3fd88
+442, 0xf8ca56415fb4e453
+443, 0xabd0899c07205e77
+444, 0x87fdc7a44b4ad50f
+445, 0xd75744911641a278
+446, 0x7c8c9a65df6fcb95
+447, 0x79d785e3c7a5b695
+448, 0x421e4565ba1f592f
+449, 0x27f87eb2517835cf
+450, 0xb62cc4297441c83e
+451, 0xd817a80ac815ca6d
+452, 0xad84388130df2aa8
+453, 0x5e6b1640452d6ac8
+454, 0x936285e15edce2a3
+455, 0x903bccc4969768e8
+456, 0xefc2cb7b109d3140
+457, 0x633e9dfdda2d903a
+458, 0x2a2f3225925678a1
+459, 0xe07eac91a27f8547
+460, 0xe50ced40eda78cb3
+461, 0xc5b22500e1c7441
+462, 0x32becf61bca3aa72
+463, 0xa2e37c4b30671344
+464, 0xc9f1c1910f45d544
+465, 0x9b50333b2dcdf730
+466, 0x310bfd53a1684b94
+467, 0x1e1dc21e66ac6455
+468, 0x81876c2bfb1ed5a1
+469, 0xd0c54a3e25eadc7b
+470, 0x3791b6fbbd5c7ba0
+471, 0x133be57356c599fc
+472, 0x8d1148eb8e83fdea
+473, 0x311aedba0d8b42cc
+474, 0x1142ae52745f94bb
+475, 0xc5f4ab2fbde8c4a3
+476, 0xd23be827b5b24f6d
+477, 0x65f95194cd122715
+478, 0x4b48969d73125922
+479, 0x46f165052b8ff988
+480, 0x5c689f94b9275ff4
+481, 0x93b03823ff2d536b
+482, 0x871f3775aa4e3523
+483, 0x5af829f7cc0f66a5
+484, 0xa32e05739cbeac8c
+485, 0xacff1856ddace0fe
+486, 0x8eeb5e7f991a5322
+487, 0x6325c2720e0dbdea
+488, 0x9fb817bc4fdf5200
+489, 0x9786f0d850e43d78
+490, 0x571f76dd7f9fb77a
+491, 0x4d9e94e181cbc63f
+492, 0x8bb632d3376c547a
+493, 0x9cc26d9efd1c88b9
+494, 0x9c5d49579df52b0b
+495, 0x6201abf7e1cda07b
+496, 0x90d68f0c6c884963
+497, 0xfc5b66188ef7f561
+498, 0x6d9303cf2e0e0f95
+499, 0xd7cfcff535f5ed07
+500, 0x14d1a1228daa4ac6
+501, 0xe00ef5762f66ae50
+502, 0xf113a79471582978
+503, 0x430985281785dc7a
+504, 0x31914108c206ed5
+505, 0x7ba6707b6419971c
+506, 0x2ec63b033ce112e5
+507, 0xf8bcd36ced3b41e3
+508, 0xe5cf908c8010414b
+509, 0xf5ee224b7c703e30
+510, 0x9a9733af0b12338b
+511, 0x83e18cc00ace34f8
+512, 0xd52cff39e23008b8
+513, 0xa700578136b9c0c5
+514, 0x3fa179d32ac51f99
+515, 0xef2d5eab6d4ad380
+516, 0x709024a5abd032df
+517, 0xc607c7ee349ede87
+518, 0x803d784e9731eb5f
+519, 0x2ef06f4ba769282d
+520, 0x4bc1dca1e9f07eb9
+521, 0x930c958a7a72f94d
+522, 0x249bc8db2cc7a3bf
+523, 0x3845305798f9a5d
+524, 0x6f137eca9ab6f948
+525, 0xc31f5a963d31bd67
+526, 0x9d39693d5383626f
+527, 0x52fb41c335a8b98e
+528, 0xb79d1a29a06006ec
+529, 0x7c0926a7a3eda2cc
+530, 0xffdf5214406fd53e
+531, 0xc6aa02a7e94282b9
+532, 0xd4a4431b4aa301ee
+533, 0x4271cc0f9420d3ab
+534, 0x26fccd7cc7fc2485
+535, 0x330594bb945b8d5a
+536, 0x6ea8eaad12e5cb8c
+537, 0x831c3467726bede3
+538, 0x31d1eb10017eaa61
+539, 0xc7aa75e41508f5cb
+540, 0xde51810f0cadd0b5
+541, 0x50e5b3e73692f80b
+542, 0x82107ec55636e188
+543, 0x9828ef175d843ab4
+544, 0xb8edc6a860dd421e
+545, 0x25c0c138fd537ac3
+546, 0x47e72a771e8eb563
+547, 0xbb0f8c5333f4a2cc
+548, 0x91750d2fb9b2d479
+549, 0xe662d8f6fe38df36
+550, 0x72a6d879fb5619f0
+551, 0x6817c7878dcbf077
+552, 0x4e7741cb484661e8
+553, 0x3b3b3ba0be5711bf
+554, 0xa6989f5d25868765
+555, 0x43c276398997e4e0
+556, 0xdcbe16a94da28870
+557, 0x454936980a699c99
+558, 0xac614bfa8f0266c6
+559, 0x9174841392e213d5
+560, 0xa0e2acffc5fc9d1f
+561, 0xe53a08a7a0e6521a
+562, 0x2b845cf7c24172e0
+563, 0x265a4fc5f7adec0d
+564, 0x1f34fbe5f1e49420
+565, 0x139181f6fb647f20
+566, 0x88c35d46e2fcd05e
+567, 0x2a6d5b55903c0459
+568, 0xcea28eb621ad7bf1
+569, 0x5c9cdc13e7aaa30
+570, 0x5fe63e14746e7103
+571, 0x7923e53d73835db9
+572, 0x376e661210bf1b06
+573, 0x5b1cab85450efdd5
+574, 0x3908dc096c70b452
+575, 0x4825e303cd1f396f
+576, 0xed476bfd702957c3
+577, 0x6acc013aff5db743
+578, 0x62c80b776343d488
+579, 0x9c75edcd5b012697
+580, 0xaa053362a3b9770a
+581, 0xa907e236c7c07e94
+582, 0x15b2c380451692c0
+583, 0x94f79142697bd61f
+584, 0xbc657d31ea98d44f
+585, 0xcbaa5e52517a1f5e
+586, 0x96aa2e44a7c4a03f
+587, 0x216d3c66db2b515d
+588, 0x157001807e3ca88a
+589, 0x52b3a596bdd3859a
+590, 0xed747e7fc5e3adac
+591, 0x78fd765ddb2c448d
+592, 0xe53dc7299ed8614e
+593, 0x75ad41fb1d7a790a
+594, 0xc14f6b944b0e6cb1
+595, 0x7c314b69fce3df1c
+596, 0xb56d82eb740d7abc
+597, 0x5132a93c41251fdb
+598, 0xe3ce35bd2a82f958
+599, 0x440571a981c722f2
+600, 0x194cdfd9f186bc9
+601, 0xb89e522a5db00939
+602, 0xad35f339f68df3c8
+603, 0xa82ab18420322293
+604, 0xaffa6df9b72b27c4
+605, 0x9615694d23beaa2c
+606, 0x1d82ebe563abad91
+607, 0xab50ef65fbd94385
+608, 0x1b070dbd70a9a14
+609, 0x2ececa796abbadf0
+610, 0x6bbeafe9e81ab2a2
+611, 0x60dcd0d2a9b76914
+612, 0x1e748039ef05c33f
+613, 0x6d4d17f2213ccdff
+614, 0x9fa56132957bc987
+615, 0x60a17185de2428eb
+616, 0xb56038ddf306479c
+617, 0x3b1db5df92d06d8b
+618, 0x24d1bba8bdedf580
+619, 0xbfb7e6740ebaa4d9
+620, 0xab31c4473e46f61d
+621, 0x6deb3cdd8fd5869f
+622, 0x23032e47746d72d6
+623, 0xa9e72d734e10f2e8
+624, 0xbffd199b6157bc23
+625, 0x29f8254df273fb62
+626, 0xb076142130ee55ec
+627, 0x5b0b08374126c309
+628, 0xea4536aae979521f
+629, 0xc064e7abec91a174
+630, 0x46133ef80c59d935
+631, 0xf0227e2da1b14160
+632, 0x675a76641e1af5a
+633, 0x2f50a069b33d198c
+634, 0x3ded5a65e1d657eb
+635, 0xbb6999b020694f6b
+636, 0x86b2f2b33487aed7
+637, 0x76e14e85f8bfb4cf
+638, 0x38f7f1e44bd4e0db
+639, 0xc1a7d41b7e80d4ae
+640, 0x1dfaaf80bbceb42e
+641, 0x3f51c11497720c2b
+642, 0xce6da1415ddb8b80
+643, 0x7377d8bcd359b5f3
+644, 0xe077208f3f810aca
+645, 0x9a06a8a2dacbffce
+646, 0xca1f99156b09b735
+647, 0x2ff9a93064d91451
+648, 0x50f3ea93f351a7ef
+649, 0x606fceccb07054de
+650, 0x7e83d6d2f8f6685d
+651, 0x78f3995291c5d407
+652, 0xd28d2460e22d0228
+653, 0x2c5636f68a0054dd
+654, 0xd9fafb1c56c8f6cb
+655, 0xe39889b5f9d74464
+656, 0x1355372bf5db2cc1
+657, 0x26768426b9ac323
+658, 0x4af1dbdc1111fd89
+659, 0x66973587943b927f
+660, 0xf86f5f50684dfb1d
+661, 0x1247d574ff79b534
+662, 0xc8039f3259210fe2
+663, 0x79b573235c92a9f5
+664, 0x213f642d8450e2f0
+665, 0x5db7706973376566
+666, 0x6182c12e69b373d7
+667, 0x3e5ac47300aec07f
+668, 0x4b5b6c57b1574376
+669, 0x6b7fcceefd56b17c
+670, 0xf656c3455cb9d4b8
+671, 0x7577e2e13329721f
+672, 0xf33c0c53ce956e8d
+673, 0x7d0f328ee356174
+674, 0x10ec9a168088686e
+675, 0x71ef1776d062dfa
+676, 0xaa7b590a488a6bc4
+677, 0x38612b6dd8049a1c
+678, 0x939045e36874f731
+679, 0xcb9d1d74c56d5ac9
+680, 0x54f1c1c8fef1d8ff
+681, 0x3ee4b85c8c7e939e
+682, 0xb9b4608e019f352c
+683, 0x79d4701275d12e6a
+684, 0x2632a2d9835c7f19
+685, 0x1662cd9fba293692
+686, 0xbcb70265115ee944
+687, 0xdc43fb9761468604
+688, 0xe3eec4e7d3871352
+689, 0x829531753226989d
+690, 0x2748cc67f540e074
+691, 0x39c4af25d607837d
+692, 0x741a243f4cb5df99
+693, 0xda1353287e18b49a
+694, 0xa6735689d751ea74
+695, 0x46326d587340ce0b
+696, 0xc18531df4550012b
+697, 0x6f7901e05dd4b818
+698, 0xfb966afc4c001d63
+699, 0x6dc10fca67a9cfdb
+700, 0xd6527ffadf0feaae
+701, 0x3b900172045e25d
+702, 0xb7dd594cdded6a46
+703, 0x6602aee7ec1599fc
+704, 0x7fbf12f23747546a
+705, 0x32e63f662bd2de0d
+706, 0xedf47770b67ed641
+707, 0x331bef83481c5c2a
+708, 0x8fc4256fdf05158c
+709, 0x98eba48dabccf5e0
+710, 0xdbc2f2cdb7b1c154
+711, 0x7777755616517ad3
+712, 0xd473c147d2628ac1
+713, 0x861e15d1d760b5a7
+714, 0xf4d25926405ecb07
+715, 0xb7739c69effff86e
+716, 0xe97fbafa6f96830c
+717, 0xf13e8a334e8bede1
+718, 0xcd60010cba4ee4f9
+719, 0x1f537ac2b82e6008
+720, 0x1fda8d781a89140a
+721, 0x9dc204f3f4a463f0
+722, 0x456dcd18eb56a1ab
+723, 0x629957bc87bd16a1
+724, 0x2c8000ddb8c75253
+725, 0xc31dae9ec8449284
+726, 0xdac05c8baa2b691a
+727, 0x21ff7be9ffa3e7ac
+728, 0x844f4b5ed4ee08d0
+729, 0x651f913fd636c994
+730, 0xca3e71a2110b2d49
+731, 0x7709bc42253ed09d
+732, 0xbb164d45b6569d43
+733, 0x90ec2f040c20a112
+734, 0xfa6e77e9166f5be4
+735, 0x6b6d12c1842d587d
+736, 0xfcd7ff8466e25e2a
+737, 0x6a5a2ed8bd971297
+738, 0x2ec35f6bba5adcbc
+739, 0xc83676e16651249a
+740, 0x458f6064cefe10ba
+741, 0x90d54d527e6cd028
+742, 0xa5613e88db27c388
+743, 0x331e0c7d85aa1abc
+744, 0x8cee4977e210358
+745, 0xfcae379aa6cbff8e
+746, 0xd1407afc97a57e86
+747, 0x1fab25c864f094ae
+748, 0xd914864a63004552
+749, 0x4214d226a20f1384
+750, 0x3f4e0d80c488b715
+751, 0xc5ca2f654024b7c8
+752, 0xc1e27a124e7c821c
+753, 0xd890a915ffc7918c
+754, 0x22fba040ce51a9f8
+755, 0xbf61cebd8891617a
+756, 0x7846609ee228e319
+757, 0x536d1854375509b8
+758, 0xbbfb45fc6e666f50
+759, 0xd85b4c0527f9d7d6
+760, 0x528cc9c7fa2a84c8
+761, 0x27a1baece647f2cb
+762, 0xfddf0cb92fe09dc3
+763, 0xeb5008fe965d8d96
+764, 0x4a3307937eb2e5c8
+765, 0xd07d74c240c6c363
+766, 0x16f62290179d1bbf
+767, 0xe99c9bcc9cb1ece7
+768, 0xc64f9be03c8a93be
+769, 0x32659effaf666c1f
+770, 0x4bb228cfb30b6672
+771, 0x98764870842068a5
+772, 0x5b12ef2d2cd8bdcc
+773, 0xbc79d1c1b41f28b8
+774, 0x97a517cf3279fc9a
+775, 0x34ffd46c1d4d6025
+776, 0x9c302307ee25c8f0
+777, 0x399604eed1f18a8
+778, 0x1c9b813c2043142a
+779, 0x2944ea5e55267fe9
+780, 0x5a8a9f5e728ea667
+781, 0x30c8440adb804a0
+782, 0xee0e6b627099a937
+783, 0x3d50757ada3c52da
+784, 0x4548916b32c813ab
+785, 0x602a186fe5bf109b
+786, 0xf0d440a2227ba304
+787, 0x5a10d4e0ca9ea32b
+788, 0x6e5eb90da13ba64c
+789, 0x4c6af8fd04241ab2
+790, 0xf9eb31d26e093006
+791, 0x5d674878839fe3ea
+792, 0x1562b55b2484e47c
+793, 0xa87188c099c1cb61
+794, 0xb7736b8aa02a3392
+795, 0x5f4b301125abb20f
+796, 0x361d566984637f44
+797, 0x68c4b3feac8bd0c3
+798, 0x7066c634dd2503c1
+799, 0xfecbf7c9441eb6ea
+800, 0xdbc26ae0fc81436b
+801, 0x9ef3e2b48252e7a4
+802, 0x31a49b4c339b37c7
+803, 0xb01b2a83cf346cf4
+804, 0xc24dc2347f82fbe3
+805, 0x134cad272dcd410f
+806, 0x61260742823ba59c
+807, 0x53ac4c193a97c730
+808, 0x9207c9833af34b52
+809, 0xa72e7ee77078d1f5
+810, 0x2e6f6e1b05936885
+811, 0x783b99ce5dbf9464
+812, 0xfdfeb6f0d027bb44
+813, 0x40eeb27096f92b0
+814, 0x5ef96ff5d4a4521f
+815, 0x5595806ae873718a
+816, 0x67d449eecf4ca1c3
+817, 0xde837ab611364f3f
+818, 0x7034c24d2b139be9
+819, 0xe21166603e0a9c86
+820, 0x935694435c1f0d51
+821, 0x6cb3bec90c126088
+822, 0x4096ef662b7a9f89
+823, 0xd2d85b8d238d8c15
+824, 0xa4ea533ce3ec59b2
+825, 0x3654729d80a2db29
+826, 0x214c4cc3906d29d4
+827, 0x201c447e7588e373
+828, 0xe8b8f0ae25f683eb
+829, 0x6744aaf5754e38af
+830, 0xd1ffb10d6f27a061
+831, 0xe536733a7b3a6c30
+832, 0x39f0f66e47cbf2c9
+833, 0x856a9593526fde2
+834, 0x2e2a817a0098ea4b
+835, 0xc5e1eeb551a0e3d3
+836, 0x3f21e2f5e2d50b2
+837, 0x906af56c66dd9f8c
+838, 0x30f6dbd70329fac8
+839, 0xc443dfddf3c01a60
+840, 0x7ab85d9aa9675470
+841, 0x8c9080bd39717bfc
+842, 0x4b1ccdb3c3597f6f
+843, 0x74e2542d70ab5d67
+844, 0xbb3d236aad00f74
+845, 0xcf3cadf9a2804774
+846, 0xe851d9750e42bd07
+847, 0xc0ad82029b1c371f
+848, 0x7ee119eb552d6c07
+849, 0xd8024049bd1d784a
+850, 0xfa67a899760363
+851, 0xaa7c2f438b178197
+852, 0xc473674a47ffe064
+853, 0x539fbe3fc674c270
+854, 0xdb48484748a76f3b
+855, 0xc73b2b092060d
+856, 0xa1d2a15345016f5d
+857, 0x4d0fe8599f9bba47
+858, 0xa0edc275e6f8f1d1
+859, 0x40590a8655bc8d72
+860, 0x35b4223161f05f75
+861, 0xa04c0c0f616752dc
+862, 0x7f371ed2ca45432d
+863, 0x2ff1a08f75ac6438
+864, 0xe2dc5c3682282f48
+865, 0xe1e4179fa98d9013
+866, 0x8cb083d6843a73d5
+867, 0xb4c2b5921b706854
+868, 0x738e14c0e7352445
+869, 0xcd2b646f91afd8c7
+870, 0xd5779a5b57a264fd
+871, 0xc39ff855586c7d07
+872, 0x3e3f0098c631a859
+873, 0x644e02fae032110
+874, 0xa8834613c0a45278
+875, 0x69482f2c08e10657
+876, 0xe4ee475bdb87e69a
+877, 0xdc1ef7b25c0d0019
+878, 0x88a3fa2be18d8744
+879, 0x60a02e0b21c5bec7
+880, 0xb6867b88aa19bc1a
+881, 0xb599409affcf10eb
+882, 0xaeaa1778a5e59daa
+883, 0xd7a91a52c16663e3
+884, 0x93cb269affe07b1c
+885, 0x841b6ced3a4ba815
+886, 0x84541768e1540a5c
+887, 0xe3943c84f83b3020
+888, 0x5de366fbd7b45258
+889, 0xd787cc3bde91a661
+890, 0x814071446edecb57
+891, 0x15d8c602a1141514
+892, 0x72f07bc8002d1d0d
+893, 0x4a8bd8dc9a1f0f3e
+894, 0x8723796ae0f20d35
+895, 0xda7283c2051f73b2
+896, 0x2df0cc247f90bd3b
+897, 0x79a8522b968f990a
+898, 0x951ede190c8b9d02
+899, 0xc512f1a5b14b018a
+900, 0xf0e3ddc03b9a4259
+901, 0x8cf4a35ad312e15f
+902, 0xebef28926b11094b
+903, 0x5628ba687325921c
+904, 0xc3aa75e57edc49c3
+905, 0xc38382fa98e762ba
+906, 0x8d209e896285848e
+907, 0x2c7d6adf592b4a3e
+908, 0x62de48e36f8338f3
+909, 0x4a752741e00de30e
+910, 0xf7855b70f1f6ec2b
+911, 0xa505fa4428199e43
+912, 0xe8b6b423b826bbac
+913, 0x4bd1206cf8786d05
+914, 0x6dcf040391fe3bf4
+915, 0x913f500f87e1bba3
+916, 0x5acf775aa180a5d5
+917, 0x74dd28d9432ce739
+918, 0x996c2ff2f0dc2495
+919, 0x73dbfe6c56effe4
+920, 0x56fddd25196f5e40
+921, 0xe87810158f5b7
+922, 0x7b8795e996383f1f
+923, 0x9ba5ee7c777c4c82
+924, 0x17ce3908d270fe1c
+925, 0x3df9e613c1aedfae
+926, 0xcdd26871b32fc8e1
+927, 0xd71cb13afc633979
+928, 0x63427c8ea9b1c79e
+929, 0xd070f7664d3b405d
+930, 0x46f2a9e32d9fb769
+931, 0xb4c3822a45e9fe9b
+932, 0x8ba30b97fe6f5ec7
+933, 0x70aa554ee2fc11f9
+934, 0xa80c99dbe0cfcfaf
+935, 0x36d9250cb2d68ed
+936, 0x2995e4b9e1cd1db4
+937, 0x4b3803ba57fc570f
+938, 0xae3959e7d740eaa5
+939, 0xb4cbd6662adbae08
+940, 0xae46576446e8dbc4
+941, 0xc4828e008a9a8a54
+942, 0x145d7db8e6554b2f
+943, 0x1b1b8916a730c371
+944, 0xdaf84b2bebe31963
+945, 0x5b59b80ef23a2403
+946, 0x9180c7e89cab6fd3
+947, 0x80e58f5411babf34
+948, 0xa06cf55185b9b005
+949, 0x13b2c798424173ad
+950, 0xc510f8e706311d49
+951, 0x1f974b83b6046d3a
+952, 0xae6e8e85e822d1c3
+953, 0x66f2c8dc3274a31a
+954, 0x7e04dbcbf65bd377
+955, 0xabf41ede01ec20a4
+956, 0x5efa0948f6bbb2ea
+957, 0xbc91c99d8592255
+958, 0xf6d6917911d86d75
+959, 0x85ce273d54e9097a
+960, 0xbdfd30f2420fff92
+961, 0x8802f02f610b537c
+962, 0xd1d70037ed543229
+963, 0x908aaf97f9693a46
+964, 0x1f6cfeaa0834d53a
+965, 0xa453fd1648ce04d2
+966, 0x2c38bb85ebc64af9
+967, 0xd2daff551c90c4f8
+968, 0xae5a0d949797d784
+969, 0xf0974c8552ac9593
+970, 0xa10b70499f65c693
+971, 0x39a449ebd594ddff
+972, 0x8ea090f2b17b9b49
+973, 0xc592de318090fd83
+974, 0xb63e4fbc467b6912
+975, 0x57a0c1c5ce0e4dcc
+976, 0xa7c517cf3d436b35
+977, 0xef6dcb0f3fad038b
+978, 0xaf4fb60315b91287
+979, 0x5e0776f67304f331
+980, 0xe927753b8e6f7932
+981, 0xd3df2dd92559e304
+982, 0xdaed52aa6af44413
+983, 0x1b59f4dac1e181f8
+984, 0x4a73c2293877ef39
+985, 0xca45d0d015fe44de
+986, 0x4659c8b7853735a8
+987, 0x12de6466bdf8adeb
+988, 0xaeea857a09bfec15
+989, 0xcc9cf4b3c0b88a23
+990, 0xa44ae52396a5e1bf
+991, 0x5847a724305d137f
+992, 0x8f4d4de223956182
+993, 0x58254dfada867a8
+994, 0x900a98222c2f339e
+995, 0xdb575260935d51d5
+996, 0x13fb4bfbbc0d7b53
+997, 0x62213850186bb92b
+998, 0x2a34823312c00388
+999, 0x6148329042f743b0

parrot/lib/python3.10/site-packages/numpy/random/tests/data/pcg64dxsm-testset-2.csv

@@ -0,0 +1,1001 @@
+seed, 0x0
+0, 0xd97e4a147f788a70
+1, 0x8dfa7bce56e3a253
+2, 0x13556ed9f53d3c10
+3, 0x55dbf1c241341e98
+4, 0xa2cd98f722eb0e0a
+5, 0x83dfc407203ade8
+6, 0xeaa083df518f030d
+7, 0x44968c87e432852b
+8, 0x573107b9cb8d9ecc
+9, 0x9eedd1da50b9daca
+10, 0xb33a6735ca451e3c
+11, 0x72830d2b39677262
+12, 0x9da8c512fd0207e8
+13, 0x1fc5c91954a2672b
+14, 0xd33479437116e08
+15, 0x9ccdd9390cee46f3
+16, 0x1fd39bb01acd9e76
+17, 0xedc1869a42ff7fe5
+18, 0xbd68ca0b42a6e7e9
+19, 0x620b67df09621b1f
+20, 0xfa11d51bd6950221
+21, 0xc8c45b36e7d28d08
+22, 0xe9c91272fbaad777
+23, 0x2dc87a143f220e90
+24, 0x6376a7c82361f49d
+25, 0x552c5e434232fe75
+26, 0x468f7f872ac195bc
+27, 0x32bed6858125cf89
+28, 0xe4f06111494d09d3
+29, 0xa5c166ffea248b80
+30, 0x4e26605b97064a3f
+31, 0xceafd9f6fc5569d
+32, 0xb772f2f9eed9e106
+33, 0x672c65e6a93534e2
+34, 0xcdc5e1a28d1bd6a0
+35, 0x1ed9c96daeebd3e3
+36, 0x4d189dcfc0c93c3f
+37, 0x50df5a95c62f4b43
+38, 0xcccf4949fa65bbb8
+39, 0x19b8073d53cdc984
+40, 0x6fb40bba35483703
+41, 0xb02de4aef86b515a
+42, 0x4d90c63655350310
+43, 0xea44e4089825b16c
+44, 0x8d676958b1f9da2b
+45, 0x6d313940917ae195
+46, 0x1b1d35a4c1dd19f4
+47, 0x117720f8397337ef
+48, 0xcc073cf3ac11eeaa
+49, 0x8331ec58a9ff8acb
+50, 0xf3dc2a308b6b866f
+51, 0x7eba1202663382b6
+52, 0x8269839debeb4e5a
+53, 0x87fd3dc0f9181a8e
+54, 0xabe62ddd3c925f03
+55, 0x7f56f146944fe8d4
+56, 0xc535972150852068
+57, 0x60b252d453bd3a68
+58, 0x4251f0134634490a
+59, 0x338950da210dfeb2
+60, 0xcadfe932971c9471
+61, 0xfb7049457fab470e
+62, 0x9bfb8145a4459dff
+63, 0x4a89dda3898f9d8a
+64, 0x88cc560151483929
+65, 0x277dc820f4b6796e
+66, 0x3524bd07ea0afb88
+67, 0x92eb6ffb2bf14311
+68, 0xf6559be0783f3fe9
+69, 0xf0844f9af54af00d
+70, 0xdd5e0b59adcef8a
+71, 0x4ff7e4f2ab18554c
+72, 0x3fa22c8a02634587
+73, 0x1db8e1a9442fe300
+74, 0x40cf15953ad3d3e7
+75, 0x92af15fe1a9f6f0a
+76, 0xab4a0e466fb0cfd
+77, 0x944f1555a06cca82
+78, 0x10cf48412f1f6066
+79, 0x7f51f9a455f9e8e1
+80, 0x47ee93530f024c7e
+81, 0x36cf2f0413e0f6f2
+82, 0xa315e23731969407
+83, 0xd8e2796327cf5f87
+84, 0xa86072696a555c34
+85, 0xee3f0b8804feaab7
+86, 0x41e80dc858f8360b
+87, 0x31ec2e9b78f5b29
+88, 0xd397fb9b8561344c
+89, 0x28081e724e649b74
+90, 0x5c135fc3fc672348
+91, 0x9a276ca70ce9caa0
+92, 0x9216da059229050a
+93, 0xcf7d375ed68007b0
+94, 0xa68ad1963724a770
+95, 0xd4350de8d3b6787c
+96, 0xee7d2c2cc275b6d2
+97, 0x71645ec738749735
+98, 0x45abdf8c68d33dbb
+99, 0xe71cadb692c705ea
+100, 0x60af6f061fd90622
+101, 0x1eabe2072632c99d
+102, 0x947dda995a402cb6
+103, 0xbb19f49a3454f3b
+104, 0xe6e43e907407758c
+105, 0xfe2b67016bd6873a
+106, 0x7fdb4dd8ab30a722
+107, 0x39d3265b0ff1a45b
+108, 0xed24c0e4fce8d0c2
+109, 0xf6e074f86faf669d
+110, 0x9142040df8dc2a79
+111, 0x9682ab16bc939a9c
+112, 0x6a4e80c378d971c8
+113, 0x31309c2c7fc2d3d6
+114, 0xb7237ec682993339
+115, 0x6a30c06bb83dccd9
+116, 0x21c8e9b6d8e7c382
+117, 0x258a24ae6f086a19
+118, 0xb76edb5be7df5c35
+119, 0x3c11d7d5c16e7175
+120, 0xbdfc34c31eff66e1
+121, 0x8af66e44be8bf3a2
+122, 0x3053292e193dec28
+123, 0xd0cc44545b454995
+124, 0x408ac01a9289d56
+125, 0x4e02d34318ec2e85
+126, 0x9413ff3777c6eb6b
+127, 0xa3a301f8e37eb3df
+128, 0x14e6306bd8d8f9f9
+129, 0xd3ea06ce16c4a653
+130, 0x170abe5429122982
+131, 0x7f9e6fddc6cacb85
+132, 0xa41b93e10a10a4c8
+133, 0x239216f9d5b6d0b5
+134, 0x985fcb6cb4190d98
+135, 0xb45e3e7c68f480c6
+136, 0xc1b2fc2e0446211c
+137, 0x4596adb28858c498
+138, 0x2dd706f3458ddc75
+139, 0x29c988c86f75464
+140, 0xac33a65aa679a60
+141, 0xa28fef762d39d938
+142, 0x541e6fa48647f53
+143, 0x27838d56b2649735
+144, 0x8e143d318a796212
+145, 0xaea6097745f586b8
+146, 0x636143330f8ee2e6
+147, 0xc2d05fd8b945b172
+148, 0x6e355f9eb4353055
+149, 0xeb64ca42e8bf282e
+150, 0xe8202dfd9da0fe5
+151, 0x7305689c9d790cba
+152, 0xf122f8b1bef32970
+153, 0x9562887e38c32ba5
+154, 0xf9cd9be121b738d
+155, 0x6238e0c398307913
+156, 0x5f2e79bb07c30f47
+157, 0x8ce8e45c465006e
+158, 0x39281fe1e99e2441
+159, 0xafb10c2ca2874fea
+160, 0x6e52f91633f83cf
+161, 0x8ff12c1ac73c4494
+162, 0xe48608a09365af59
+163, 0xefd9bbc7e76e6a33
+164, 0xbe16a39d5c38ec92
+165, 0x6a6ffbcaf5a2330f
+166, 0xdd5d6ac7d998d43d
+167, 0x207bf978226d4f11
+168, 0xf8eec56bd2a0f62e
+169, 0xa5bccf05dce0d975
+170, 0x93cf3ec1afe457a6
+171, 0x38651466d201f736
+172, 0x3ad21473985c9184
+173, 0xc6407a3bd38c92a6
+174, 0xb1ec42c7afa90a25
+175, 0xbdeca984df8b7dd3
+176, 0xb6926b1d00aa6c55
+177, 0x86141d0022352d49
+178, 0x169316256135ee09
+179, 0xffb1c7767af02a5c
+180, 0x502af38ad19f5c91
+181, 0xfbf6cbc080086658
+182, 0x33cf9b219edae501
+183, 0x46e69bebd77b8862
+184, 0xf11e0cc91125d041
+185, 0xb4cd1649f85e078f
+186, 0xb49be408db4e952
+187, 0xb0b8db46140cce3c
+188, 0xba647f2174012be7
+189, 0x4f0a09e406970ac9
+190, 0xf868c7aec9890a5c
+191, 0xde4c8fa7498ea090
+192, 0x872ceb197978c1d4
+193, 0x1eb5cd9c3269b258
+194, 0x3ea189f91724f014
+195, 0x41379656f7746f2c
+196, 0x7bd18493aca60e51
+197, 0x5380c23b0cbbf15e
+198, 0x920b72835f88246b
+199, 0x24d7f734a4548b8e
+200, 0x9944edb57e5aa145
+201, 0x4628e136ebb8afe1
+202, 0xb4ee6a776356e2a7
+203, 0x481cbe9744ccf7d7
+204, 0x7e8d67e8b0b995d9
+205, 0xeeacde100af7b47e
+206, 0x103da08f2487dab7
+207, 0x6b9890a91d831459
+208, 0xd0c5beae37b572c7
+209, 0xfdccc371ee73fcc
+210, 0x65438f0a367a2003
+211, 0x5d23b2c818a7e943
+212, 0x9a8ed45ac04b58b3
+213, 0xdaf3c3f1695dce10
+214, 0x5960eec706fa2bc0
+215, 0x98ca652facb80d40
+216, 0x72970ae5e2194143
+217, 0x18c6374d878c5c94
+218, 0x20fa51f997381900
+219, 0x3af253dba26d6e1d
+220, 0x1b23d65db15c7f78
+221, 0x9f53ae976259b0e3
+222, 0x9a6addb28dc92d49
+223, 0x1e085c4accd0a7d7
+224, 0xe9d3f4cc9bad6ce5
+225, 0xe018fad78b5b1059
+226, 0x5ef7682232b4b95
+227, 0xb2242aa649f5de80
+228, 0x8f3e6d8dd99b9e4e
+229, 0xb9be6cc22949d62a
+230, 0xecbdc7beaa5ff1fe
+231, 0xd388db43a855bdf0
+232, 0xd71ee3238852568d
+233, 0x85ab3056304c04b5
+234, 0x2ed7ae7ad3cfc3cb
+235, 0x781d1b03d40b6c48
+236, 0x7d3c740886657e6d
+237, 0x982cfa6828daa6b0
+238, 0x278579599c529464
+239, 0x773adecfae9f0e08
+240, 0x63a243ea4b85c5d7
+241, 0x59940074fc3709e1
+242, 0xc914a2eed58a6363
+243, 0x2602b04274dd724c
+244, 0xdf636eb7636c2c42
+245, 0x891a334d0d26c547
+246, 0xde8cd586d499e22d
+247, 0x3ea1aa4d9b7035b6
+248, 0xd085cff6f9501523
+249, 0xe82a872f374959e
+250, 0x55cb495bbd42cc53
+251, 0x5f42b3226e56ca97
+252, 0xea463f6f203493a3
+253, 0xeef3718e57731737
+254, 0x1bd4f9d62b7f9f3c
+255, 0x19284f5e74817511
+256, 0xaf6e842c7450ca87
+257, 0x1d27d2b08a6b3600
+258, 0xfb4b912b396a52e3
+259, 0x30804d4c5c710121
+260, 0x4907e82564e36338
+261, 0x6441cf3b2900ddb7
+262, 0xd76de6f51988dc66
+263, 0x4f298ef96fd5e6d2
+264, 0x65432960c009f83d
+265, 0x65ebed07e1d2e3df
+266, 0xf83ee8078febca20
+267, 0x7bb18e9d74fc5b29
+268, 0x597b5fbc2261d91
+269, 0xea4f8ed0732b15b2
+270, 0xba2267f74f458268
+271, 0x3f304acabd746bbb
+272, 0x7bd187af85659a82
+273, 0x88e20dbdb7a08ea3
+274, 0x2a2dc948c772fcb4
+275, 0x87784fec2993c867
+276, 0x89163933cd362d4e
+277, 0xfd7b24f04302f957
+278, 0x9bdd544405dfb153
+279, 0xddee0fac58ffc611
+280, 0xa8e8993417e71ec1
+281, 0x55e0ab46ff7757af
+282, 0x53e7645f08d3d7df
+283, 0xbf78e563bc656ba2
+284, 0x1d162253b45ee2de
+285, 0x15e2bfefedf29eb4
+286, 0x4e2a4584aa394702
+287, 0xa89fb12b01525897
+288, 0x825bd98f0544e4df
+289, 0xfc6c50da6750700
+290, 0xc24aaabde7d28423
+291, 0x79d6f4660fcb19e5
+292, 0xee7d4fb40c8d659f
+293, 0x70bc281b462e811d
+294, 0x23ed4dc9636519a7
+295, 0xcb7c3f5a5711b935
+296, 0xe73090e0508c5d9d
+297, 0xb25a331f375952a6
+298, 0xa64c86e0c04740f6
+299, 0xb8f3ffc8d56ac124
+300, 0x2479266fc5ee6b15
+301, 0x8d5792d27f5ffbcb
+302, 0xb064298be946cd52
+303, 0xf0934a98912ffe26
+304, 0xbe805682c6634d98
+305, 0xe0e6e2c010012b4f
+306, 0x58c47d475f75976
+307, 0x358c9a6e646b2b4a
+308, 0x7e7c4ffca5b17ba7
+309, 0x43585c8c9a24a04c
+310, 0x5154ddbcd68d5c2c
+311, 0x4a2b062d3742a5e
+312, 0xca5691191da2b946
+313, 0x696a542109457466
+314, 0x9eb5d658a5022ba5
+315, 0x8158cf6b599ab8dc
+316, 0x1b95391eaa4af4a6
+317, 0x9953e79bd0fc3107
+318, 0x8639690086748123
+319, 0x2d35781c287c6842
+320, 0x393ef0001cd7bc8f
+321, 0xe3a61be8c5f2c22a
+322, 0x5e4ff21b847cc29b
+323, 0x4c9c9389a370eb84
+324, 0xd43a25a8fc3635fa
+325, 0xf6790e4a85385508
+326, 0x37edf0c81cb95e1d
+327, 0x52db00d6e6e79af8
+328, 0x3b202bceeb7f096
+329, 0x2a164a1c776136bb
+330, 0x73e03ee3fd80fd1b
+331, 0xd2c58c0746b8d858
+332, 0x2ed2cb0038153d22
+333, 0x98996d0fc8ceeacc
+334, 0xa4ed0589936b37f
+335, 0x5f61cf41a6d2c172
+336, 0xa6d4afb538c110d7
+337, 0xe85834541baadf1a
+338, 0x4c8967107fd49212
+339, 0x49bafb762ab1a8c1
+340, 0x45d540e2a834bf17
+341, 0x1c0ec8b4ed671dac
+342, 0x3d503ce2c83fe883
+343, 0x437bfffd95f42022
+344, 0xc82d1e3d5c2bc8d2
+345, 0x7a0a9cbfcb0d3f24
+346, 0xc0a4f00251b7a3be
+347, 0xb5be24e74bb6a1c6
+348, 0xa3104b94b57545b1
+349, 0x86de7d0c4b97b361
+350, 0x879c1483f26538a6
+351, 0xd74c87557f6accfb
+352, 0x2f9be40dbf0fe8a1
+353, 0x445a93398f608d89
+354, 0x7b3cb8a7211d7fdc
+355, 0xe86cc51290d031e7
+356, 0x33ef3594052ad79f
+357, 0xc61911d241dbb590
+358, 0x37cccb0c0e3de461
+359, 0xb75259124080b48b
+360, 0xd81e8961beb4abe5
+361, 0xf4542deb84a754e
+362, 0x6ea036d00385f02e
+363, 0xa7b60b0ac3b88681
+364, 0x108a6c36ca30baf5
+365, 0x4a2adc5bbfe2bf07
+366, 0x4079501f892a5342
+367, 0x55e113963c5448f0
+368, 0x8019ff4903b37242
+369, 0x109c6dcdb7ec6618
+370, 0x1239ac50944da450
+371, 0xe1399c7f94c651c1
+372, 0x5a6bbbae388d365a
+373, 0x4d72be57b8810929
+374, 0x3f067df24384e1fb
+375, 0x4f8b9e0f7f6c7be
+376, 0x202492c342a3b08
+377, 0x250753192af93a3
+378, 0xfba1159d9de2cb8e
+379, 0xba964497ab05505c
+380, 0x1329ec5d8a709dca
+381, 0x32927cacb6cd22bb
+382, 0x6b4d7db904187d56
+383, 0xe76adccf8e841e02
+384, 0x8c4bf4b6a788202
+385, 0x3013a3b409831651
+386, 0x7427d125c475412f
+387, 0x84dcc4bb2bf43202
+388, 0x117526f1101372a5
+389, 0xfe95d64b8984bd72
+390, 0x524e129934cc55c1
+391, 0xc3db4b0418c36d30
+392, 0xe1cb2047e9c19f7a
+393, 0xea43d6c8d8982795
+394, 0xe80ac8a37df89ed
+395, 0xfecc2104329ed306
+396, 0xa5c38aac9c1d51ea
+397, 0x3abe5d1c01e4fe17
+398, 0x717a805d97fcc7ac
+399, 0x94441f8207a1fb78
+400, 0x22d7869c5f002607
+401, 0x349e899f28c3a1b9
+402, 0x5639950cdea92b75
+403, 0x7e08450497c375b
+404, 0x94bf898b475d211d
+405, 0x75c761a402375104
+406, 0x1930920ec9d2a1e7
+407, 0xb774ba1bc6f6e4e2
+408, 0xf715602412e5d900
+409, 0x87bb995f4a13f0ba
+410, 0xa3c787868dfa9c8d
+411, 0xa17fd42a5a4f0987
+412, 0x4a9f7d435242b86
+413, 0x240364aff88f8aef
+414, 0xe7cd4cf4bf39f144
+415, 0xd030f313ca4c2692
+416, 0xc46696f4e03ec1e9
+417, 0x22c60f1ec21060b3
+418, 0x16c88058fd68986f
+419, 0x69ca448e8e6bde3f
+420, 0x3466c2cdec218abd
+421, 0x837ac4d05e6b117d
+422, 0x911210e154690191
+423, 0x9ece851d6fa358b7
+424, 0x42f79cb0c45e7897
+425, 0xbf7583babd7c499b
+426, 0x2059fe8031c6e0b9
+427, 0xabbec8fc00f7e51d
+428, 0x88809d86a3a256e1
+429, 0xd36056df829fdcb5
+430, 0x515632b6cb914c64
+431, 0xba76d06c2558874
+432, 0x632c54ca4214d253
+433, 0xadec487adf2cb215
+434, 0x521e663e1940513d
+435, 0xb1b638b548806694
+436, 0xbe2d5bfbe57d2c72
+437, 0x8b89e7719db02f7
+438, 0x90ba5281c1d56e63
+439, 0x899e1b92fceea102
+440, 0xf90d918e15182fa6
+441, 0x94a489ce96c948c4
+442, 0xad34db453517fcd4
+443, 0xc5264eb2de15930f
+444, 0x101b4e6603a21cee
+445, 0xef9b6258d6e85fff
+446, 0x6075c7d6c048bd7a
+447, 0x6f03232c64e438aa
+448, 0x18c983d7105ee469
+449, 0x3ffc23f5c1375879
+450, 0xbc1b4a00afb1f9f
+451, 0x5afa6b2bb8c6b46e
+452, 0xe7fce4af2f2c152a
+453, 0x5b00ab5c4b3982c7
+454, 0x2d4b0c9c0eb4bd0c
+455, 0x61d926270642f1f2
+456, 0x7219c485c23a2377
+457, 0x7e471c752fecd895
+458, 0x23c4d30a4d17ba1f
+459, 0x65cb277fe565ca22
+460, 0xcbb56ed9c701363b
+461, 0xfd04ab3a6eba8282
+462, 0x19c9e5c8bab38500
+463, 0xea4c15227676b65b
+464, 0x20f3412606c8da6f
+465, 0xb06782d3bf61a239
+466, 0xf96e02d5276a9a31
+467, 0x835d256b42aa52a6
+468, 0x25b09151747f39c1
+469, 0x64507386e1103eda
+470, 0x51cbc05716ef88e4
+471, 0x998cd9b7989e81cc
+472, 0x9d7115416bec28d1
+473, 0xc992ca39de97906b
+474, 0xd571e6f7ca598214
+475, 0xafc7fb6ccd9abbf8
+476, 0x88ef456febff7bf4
+477, 0xdbe87ccc55b157d2
+478, 0xaab95e405f8a4f6d
+479, 0xad586a385e74af4f
+480, 0x23cd15225c8485aa
+481, 0x370940bf47900ac7
+482, 0xefd6afda1a4b0ead
+483, 0x9cb1a4c90993dd7a
+484, 0xff7893e8b2f70b11
+485, 0xb09e1807c0638e8e
+486, 0xb10915dcb4978f74
+487, 0x88212ab0051a85eb
+488, 0x7af41b76e1ec793f
+489, 0x2e5c486406d3fefd
+490, 0xebe54eff67f513cc
+491, 0xab6c90d0876a79b8
+492, 0x224df82f93fe9089
+493, 0xc51c1ce053dc9cd2
+494, 0x5ef35a4d8a633ee7
+495, 0x4aca033459c2585f
+496, 0xd066932c6eefb23d
+497, 0x5309768aab9a7591
+498, 0xa2a3e33823df37f9
+499, 0xcec77ff6a359ee9
+500, 0x784dc62d999d3483
+501, 0x84e789fb8acc985d
+502, 0xd590237e86aa60f
+503, 0x737e2ffe1c8ad600
+504, 0xc019c3a39a99eab8
+505, 0x6a39e9836964c516
+506, 0xe0fe43129535d9da
+507, 0xdfc5f603d639d4de
+508, 0x7b9a7d048a9c03b6
+509, 0xbb5aa520faa27fdd
+510, 0x2a09b4200f398fa2
+511, 0x38cc88107904064e
+512, 0xa9a90d0b2d92bb25
+513, 0x9419762f87e987e3
+514, 0x1a52c525153dedcd
+515, 0xc26d9973dd65ae99
+516, 0x8e89bd9d0dc6e6a1
+517, 0x2f30868dc01bfb53
+518, 0x20f09d99b46501c4
+519, 0x78b468a563b8f1e9
+520, 0xcccf34b0b6c380c7
+521, 0xf554e7dc815297e6
+522, 0x332a585cfb4a50ef
+523, 0xa9fb64a2b6da41d7
+524, 0xdcd2a5a337391ce0
+525, 0x8a9bd3e324c6463d
+526, 0x9f4487d725503bdd
+527, 0xf72282d82f1d0ff
+528, 0x308f4160abb72d42
+529, 0x648de1db3a601b08
+530, 0x36cab5192e7ebd39
+531, 0x7975fbe4ab6a1c66
+532, 0xd515b4d72243864e
+533, 0x43a568f8b915e895
+534, 0x15fa9f2057bdb91d
+535, 0x7a43858ef7a222dc
+536, 0x17b4a9175ac074fe
+537, 0xa932c833b8d0f8f8
+538, 0x1d2db93a9a587678
+539, 0x98abd1d146124d27
+540, 0xf0ab0431671740aa
+541, 0xa9d182467540ad33
+542, 0x41c8a6cfc331b7fc
+543, 0xa52c6bd0fcd1d228
+544, 0x2773c29a34dc6fa3
+545, 0x3098230746fc1f37
+546, 0xd63311bb4f23fabe
+547, 0x6712bf530cd2faec
+548, 0x342e8f342e42c4dd
+549, 0xfbd83331851cdcad
+550, 0xe903be1361bbc34d
+551, 0xd94372e5077e3ef9
+552, 0x95aaa234f194bd8
+553, 0x20c0c8fb11e27538
+554, 0xfaf47dc90462b30b
+555, 0x8ddc6d144147682a
+556, 0xf626833fd926af55
+557, 0x5df93c34290d1793
+558, 0xb06a903e6e9fca5e
+559, 0x10c792dc851d77ca
+560, 0xd9b1b817b18e56cb
+561, 0x3a81730c408eb408
+562, 0x65052c04a8d4b63c
+563, 0x3328546598e33742
+564, 0xeca44a13f62d156d
+565, 0x69f83d1d86b20170
+566, 0x937764200412027d
+567, 0xc57eb1b58df0f191
+568, 0xa1c7d67dce81bc41
+569, 0x8e709c59a6a579ce
+570, 0x776a2f5155d46c70
+571, 0xd92906fbbc373aa5
+572, 0xe97ad478a2a98bf6
+573, 0xc296c8819ac815f
+574, 0x613ede67ba70e93e
+575, 0xe145222498f99cde
+576, 0xafcdfa7a3c1cf9bf
+577, 0x1c89252176db670d
+578, 0xad245eda5c0865ff
+579, 0x249463d3053eb917
+580, 0xc9be16d337517c0b
+581, 0xefcc82bf67b8f731
+582, 0x1e01577d029e0d00
+583, 0xad9c24b2a4f3d418
+584, 0xed2cceb510db4d0f
+585, 0xbddadcdb92400c70
+586, 0x67d6b0476ef82186
+587, 0xbc7662ff7bf19f73
+588, 0x9d94452a729e6e92
+589, 0x6b278d8594f55428
+590, 0x6c4b31cceb1b2109
+591, 0xccc6c3a726701e9
+592, 0x6bc28ece07df8925
+593, 0xc0422b7bf150ccc4
+594, 0xab7158f044e73479
+595, 0xdf3347546d9ed83f
+596, 0x3b3235a02c70dff4
+597, 0x2551c49c14ea8d77
+598, 0xee2f7f5bb3cc228e
+599, 0x39b87bfe8c882d39
+600, 0x7dd420fad380b51c
+601, 0xffe64976af093f96
+602, 0x4a4f48dc6e7eaa5f
+603, 0x85f2514d32fdc8cc
+604, 0x1ab1215fd7f94801
+605, 0x4cd1200fc795b774
+606, 0xcf8af463a38942ee
+607, 0x319caa7ce3022721
+608, 0x8cd9798a76d1aea4
+609, 0x2bd3933ac7afd34e
+610, 0x85d4c323403cf811
+611, 0xd7b956d3064efa30
+612, 0x67a078dbf1f13068
+613, 0x665fa6c83e87c290
+614, 0x9333ac2416d2469b
+615, 0xdfb1fd21a0094977
+616, 0xa1962a6e2c25f8ff
+617, 0x1f3b10a7ed5287cf
+618, 0x70641efb3d362713
+619, 0xe527a2cf85d00918
+620, 0x9741e45d3f9890a3
+621, 0x6cb74b5d4d36db4b
+622, 0xf24734d622bd2209
+623, 0xadd6d94f78e9d378
+624, 0xc3bbdb59225cca7f
+625, 0x5ad36614275b30cd
+626, 0x495568dd74eea434
+627, 0xf35de47e0ffe1f2d
+628, 0xefa209dca719ab18
+629, 0x844ddcaeb5b99ae8
+630, 0x37449670a1dc7b19
+631, 0x5a4612c166f845c1
+632, 0xe70f7782f2087947
+633, 0x98d484deac365721
+634, 0x705302198cf52457
+635, 0x7135ae0f5b77df41
+636, 0x342ac6e44a9b6fc3
+637, 0x2713fd2a59af5826
+638, 0x6e1a3f90f84efa75
+639, 0x9fb3b4dd446ca040
+640, 0x530044ae91e6bd49
+641, 0xe984c4183974dc3e
+642, 0x40c1fa961997d066
+643, 0xb7868250d8c21559
+644, 0x8bc929fa085fd1de
+645, 0x7bdb63288dc8733e
+646, 0xac4faad24326a468
+647, 0x1c6e799833aea0b1
+648, 0xcc8a749e94f20f36
+649, 0x4e7abfd0443547c5
+650, 0xb661c73bb8caa358
+651, 0x4a800f5728ff2351
+652, 0x8c15e15189b9f7ed
+653, 0xab367846b811362c
+654, 0x4ba7508f0851ca2a
+655, 0xe9af891acbafc356
+656, 0xbdebe183989601f8
+657, 0x4c665ea496afc061
+658, 0x3ca1d14a5f2ed7c
+659, 0xfbdff10a1027dd21
+660, 0xdfd28f77c8cff968
+661, 0xc4fbaadf8a3e9c77
+662, 0xdac7e448b218c589
+663, 0xb26390b5befd19e2
+664, 0xd2ef14916c66dba9
+665, 0xfab600284b0ff86b
+666, 0xf04a1c229b58dabb
+667, 0xc21c45637e452476
+668, 0xd1435966f75e0791
+669, 0xc1f28522eda4a2d0
+670, 0x52332ae8f1222185
+671, 0x81c6c0790c0bf47e
+672, 0xfebd215e7d8ffb86
+673, 0x68c5dce55dbe962b
+674, 0x231d09cb0d2531d1
+675, 0x3218fba199dbbc6b
+676, 0x8f23c535f8ea0bf6
+677, 0x6c228963e1df8bd9
+678, 0x9843c7722ed153e3
+679, 0xd032d99e419bddec
+680, 0xe2dca88aa7814cab
+681, 0x4d53fb8c6a59cdc2
+682, 0x8fb3abc46157b68b
+683, 0xa3e733087e09b8e
+684, 0x6bdc1aee029d6b96
+685, 0x4089667a8906d65b
+686, 0x8f3026a52d39dd03
+687, 0x6d2e0ccb567bae84
+688, 0x74bad450199e464
+689, 0xf114fb68a8f300d5
+690, 0xc7a5cc7b374c7d10
+691, 0xf0e93da639b279d1
+692, 0xb9943841ad493166
+693, 0x77a69290455a3664
+694, 0x41530da2ebea054b
+695, 0xe8f9fab03ea24abf
+696, 0xaa931f0c9f55a57a
+697, 0xb4d68a75d56f97ae
+698, 0x3d58ff898b6ba297
+699, 0x49d81e08faf5a3f5
+700, 0xfc5207b9f3697f3b
+701, 0xa25911abb3cf19b7
+702, 0x6b8908eb67c3a41
+703, 0xd63ef402e2e3fa33
+704, 0x728e75d3f33b14c5
+705, 0x248cb1b8bc6f379a
+706, 0x3aa3d6d2b8c72996
+707, 0x49cc50bd2d3d2860
+708, 0xb4e1387647c72075
+709, 0x435a1630a4a81ed3
+710, 0xa5ea13005d2460cf
+711, 0xc7a613df37d159ec
+712, 0x95721ccc218b857e
+713, 0xd4b70d8c86b124d3
+714, 0x2b82bcc4b612d494
+715, 0xaf13062885276050
+716, 0xcbd8fcf571a33d9c
+717, 0x3f7f67ca1125fc15
+718, 0xddf4bb45aac81b4c
+719, 0x23606da62de9c040
+720, 0xa3a172375666b636
+721, 0x292f87387a6c6c3c
+722, 0xd1d10d00c5496fe1
+723, 0x86b0411ce8a25550
+724, 0x38e0487872e33976
+725, 0x363e49f88ddfd42c
+726, 0x45bdf1e9f6b66b0a
+727, 0x8a6fff3de394f9b5
+728, 0x8502158bb03f6209
+729, 0x22e24d16dba42907
+730, 0x3fe3ba427cc2b779
+731, 0x77144793f66b3d7e
+732, 0xcf8912ccb29b8af9
+733, 0xdc856caff2abd670
+734, 0xe6d3ae0b0d9d4c8b
+735, 0xb8f5d40e454c539f
+736, 0x79ca953114fbc6b7
+737, 0x478d6f4bbfa38837
+738, 0x9babae1a3ffdc340
+739, 0x40edd56802bae613
+740, 0x97a56c2dcccf0641
+741, 0xafc250257f027f8e
+742, 0x8da41ef1edf69125
+743, 0x6574b0280ff9d309
+744, 0x197c776151b8f820
+745, 0x6b03e077c9dac3b6
+746, 0x24a40ebbc5c341c5
+747, 0x50e585169a6a1c4b
+748, 0x37783a5a6a3e4e02
+749, 0xb3de81ee6fbad647
+750, 0xf4f292f57ca4591e
+751, 0x6214e9e7d44d30a
+752, 0x5920190c56d21c12
+753, 0x9ac163419b5e0c9b
+754, 0xfc2328761ae8ed93
+755, 0xc68f945b545508c6
+756, 0x687c49a17ce0a5e2
+757, 0x276d8f53d30d4ab4
+758, 0x8201804970343ce1
+759, 0x1b5d323cc2e7fb7e
+760, 0x6f351ef04fd904b
+761, 0x6c793a7d455d5198
+762, 0x46f5d108430ae91f
+763, 0xac16a15b2a0cf77f
+764, 0xa0d479d9e4122b9d
+765, 0x3afd94604307f19
+766, 0x2573ed6d39d38dbf
+767, 0xa58e14ba60b4294b
+768, 0xe69c1aed5840d156
+769, 0x4cf6fda7f04855c2
+770, 0x2fb65a56ef5f22da
+771, 0xf95819434d5dc220
+772, 0x29c65133623dafba
+773, 0x8e997bd018467523
+774, 0xfd08ba9d498461a7
+775, 0xdd52243bc78a5592
+776, 0x39c30108f6db88b3
+777, 0x38af8e1894f259b9
+778, 0x97eedf3b4ae5f6de
+779, 0x757825add80c5ece
+780, 0xf0fdd90ac14edb14
+781, 0xbbb19d4cc8cac6d4
+782, 0x9a82234edfae05e3
+783, 0x704401c61d1edf1c
+784, 0x8b0eb481fb3a1fb2
+785, 0xef6f36e7cc06c002
+786, 0x7a208b17e04b8cd7
+787, 0xf20e33d498838fe9
+788, 0xc2bdb22117058326
+789, 0x6ec31939eb4ca543
+790, 0x6f1654838f507a21
+791, 0xc65ab81a955d2b93
+792, 0x40b1420fdd9531b8
+793, 0xe31f221cab9f4f40
+794, 0x798cdd414c1deb7a
+795, 0x9c84e9c7d41cd983
+796, 0x63d6b1ae3b60b7fa
+797, 0xb42bfdd1a2f78ffa
+798, 0x37e431eaccaaa8e9
+799, 0x7508142a0f73eac9
+800, 0x91662a023df5893a
+801, 0x59782070e2fe3031
+802, 0xb2acd589a8ce7961
+803, 0xa224743fa877b292
+804, 0xaa5362aa27e6ed9e
+805, 0xa394a4e520c0c1c7
+806, 0xe49b16d2018ffb6f
+807, 0xb8074b9f2f1e762b
+808, 0xcf5f86143d5c23a7
+809, 0xfd838785db987087
+810, 0x31b1889df389aff8
+811, 0x30aaca876a4383b
+812, 0x1731bb71c4c38d4f
+813, 0x9a83a65395e05458
+814, 0x99cd0c8d67c8f4fc
+815, 0xfbd9fdc849b761a5
+816, 0x82c04834fc466889
+817, 0xdeef9d6e715e8c97
+818, 0x549c281c16da6078
+819, 0x2d70661254ad599d
+820, 0x57995793a72acac
+821, 0xf1727005116183ba
+822, 0xa22bb38945285de3
+823, 0x4f2d687fe45131ff
+824, 0x5666c87ddbbc981f
+825, 0xbcb4b2d4e7a517d0
+826, 0x5e794dd2e20b785d
+827, 0x449ad020149e093c
+828, 0x7704ee0412d106f5
+829, 0x83cbdf257b072ac1
+830, 0xae5c4fc9f638b0da
+831, 0x7b9e5a64e372ed47
+832, 0x7eddbbb22c2cdf57
+833, 0x3f19ebfa155b08e
+834, 0x91d991154dfd7177
+835, 0x611ae74b952d387f
+836, 0x3fdf7a335bda36ee
+837, 0xdf182433fc7a7c05
+838, 0x62c78598d1f8db0a
+839, 0xc3750c69d2c5c1f0
+840, 0xf1318024709efdee
+841, 0xaa3fd360d224dc29
+842, 0x62af53b2f307c19
+843, 0xdf527683c58120c2
+844, 0x3281deecc496f93d
+845, 0x4f704ad31527ef08
+846, 0x127a14a5e07cfdfc
+847, 0x90d0b1f549255c92
+848, 0xbc3406b212c5e1fc
+849, 0x4e89f39379dba91d
+850, 0x1290ef43c4998e6e
+851, 0xecfeb1a1cb1c6e1b
+852, 0x2067e90403003bf1
+853, 0x38ae04be30bdbeba
+854, 0x8a3537f298baedda
+855, 0xd07f3b825cdb2936
+856, 0xea020b5aebae8b45
+857, 0xfcd614ab031132b0
+858, 0x5fb682a4ff2268f5
+859, 0xd1c4662ce65596f4
+860, 0x7026b8270dd0b8dc
+861, 0x8101ec4b4beae45a
+862, 0xa0e9dc87940610a6
+863, 0x83ec33679d83165b
+864, 0x981847ca82e86d41
+865, 0xda84c188a304a0b7
+866, 0x3c37529c5a5bbbb8
+867, 0x34a8491ce3e19a5a
+868, 0xd36ad716a2fa6cb8
+869, 0xfd1d1d6a5189a15c
+870, 0x9716eb47851e8d8d
+871, 0x7dfb13ea3b15c5aa
+872, 0xbdf6e707f45113a5
+873, 0xb8118261b04bd097
+874, 0x6191f9895881bec6
+875, 0x7aac257ae11acf9b
+876, 0x35a491e1537ff120
+877, 0xe078943432efa71c
+878, 0xb3338485dd3dc2b9
+879, 0x456060975d2bb3b5
+880, 0xaddc4c451bdfc44c
+881, 0x18bfa7beacf96430
+882, 0x8802ebcaf0f67498
+883, 0xad922a5a825bd780
+884, 0x9fb4587d748f4efa
+885, 0xdb2a445136cd5e7
+886, 0xb98b3676ea8e96ac
+887, 0xb02d8d244d784878
+888, 0xa1a8442b18860abb
+889, 0x6a3029ba1361e5d1
+890, 0xf426d5fac161eb1
+891, 0xfa5ac2b87acecb23
+892, 0xaa659896e50535df
+893, 0xf40dd7a3d3c5c8ed
+894, 0x3f8367abecb705bc
+895, 0x2d60e7525873358f
+896, 0xc4a9d3948a0c3937
+897, 0x5ecc04fef6003909
+898, 0x7a865004918cba2
+899, 0x47ae110a678ec10b
+900, 0xa0f02f629d91aa67
+901, 0x4848b99e7fac9347
+902, 0xaa858346d63b80ac
+903, 0xeb5bf42ee161eeef
+904, 0x4d35d723d3c6ba37
+905, 0xdf22ca6ca93b64a7
+906, 0x9d198520f97b25b1
+907, 0x3068415350778efe
+908, 0xf3709f2e8793c2fe
+909, 0xd1517bac8dd9f16f
+910, 0xfb99bccaa15861dc
+911, 0xa9ad607d796a2521
+912, 0x55d3793d36bd22e4
+913, 0xf99270d891ff7401
+914, 0x401750a5c4aa8238
+915, 0xd84b3003e6f28309
+916, 0x8a23798b5fa7c98b
+917, 0xadd58bbc8f43e399
+918, 0xbd8c741ada62c6a8
+919, 0xbdc6937bc55b49fa
+920, 0x4aefa82201b8502
+921, 0x17adf29a717b303
+922, 0xa6ed2197be168f6c
+923, 0x1ba47543f4359a95
+924, 0xe34299949ac01ae9
+925, 0x711c76cffc9b62f3
+926, 0xbac259895508a4b7
+927, 0x3c8b3b3626b0d900
+928, 0x1a8d23fbe2ae71bf
+929, 0xca984fa3b5a5c3a1
+930, 0xb1986ab7521a9c93
+931, 0xd6b5b2c8d47a75b5
+932, 0xc7f1c4a88afb4957
+933, 0xdeb58033a3acd6cc
+934, 0xabe49ddfe1167e67
+935, 0x8d559c10205c06e3
+936, 0xea07a1a7de67a651
+937, 0xcbef60db15b6fef8
+938, 0xbfca142cff280e7
+939, 0x362693eba0732221
+940, 0x7463237e134db103
+941, 0x45574ddb5035e17a
+942, 0xfc65e0cb9b94a1aa
+943, 0x3154c55f1d86b36d
+944, 0x2d93a96dd6ab2d8b
+945, 0xbe3bc1d1f2542a25
+946, 0xdd4b541f7385bdaa
+947, 0x3b56b919d914e3f8
+948, 0x82fd51468a21895f
+949, 0x8988cf120731b916
+950, 0xa06a61db5fb93e32
+951, 0x6ed66c1b36f68623
+952, 0x875ae844d2f01c59
+953, 0x17ccd7ac912e5925
+954, 0x12fe2a66b8e40cb1
+955, 0xf843e5e3923ad791
+956, 0xa17560f2fd4ef48
+957, 0x27a2968191a8ee07
+958, 0xa9aab4d22ff44a3c
+959, 0x63cd0dcc3bb083ae
+960, 0x7a30b48c6160bf85
+961, 0x956160fb572503b3
+962, 0xc47f6b7546640257
+963, 0xaf4b625f7f49153
+964, 0x2f5c86a790e0c7e8
+965, 0xb52e0610ae07f0b8
+966, 0x38a589292c3d849e
+967, 0xc3e9ef655d30b4ef
+968, 0xb5695f765cda998a
+969, 0xde5d5e692a028e91
+970, 0x839476721555f72e
+971, 0x48b20679b17d9ebf
+972, 0xe3d4c6b2c26fb0df
+973, 0xce5a9834f0b4e71f
+974, 0x533abb253d5d420e
+975, 0x9eac5ad9aed34627
+976, 0xc0f2a01ab3c90dbb
+977, 0x6528eda93f6a066c
+978, 0xc16a1b625e467ade
+979, 0x1a4a320fb5e8b098
+980, 0x8819cccd8b4ab32f
+981, 0x42daa88531fd0bfd
+982, 0xcf732226409be17c
+983, 0xfddcdb25ccbf378c
+984, 0x9b15b603bf589fc1
+985, 0x2436066b95d366fe
+986, 0x8d42eff2e9cbda90
+987, 0x694b2fc8a4e8303c
+988, 0x8e207f98aaea3ccd
+989, 0x4730d7a620f822d9
+990, 0x468dc9ca30fe2fd4
+991, 0x74b36d8a1c0f031b
+992, 0x3c1aac1c488c1a94
+993, 0x19d0101042444585
+994, 0x8ec50c56d0c8adf4
+995, 0x721ec629e4d66394
+996, 0x3ca5ad93abeac4a4
+997, 0xaaebc76e71592623
+998, 0x969cc319e3ed6058
+999, 0xc0a277e3b2bfc3de

parrot/lib/python3.10/site-packages/numpy/random/tests/data/philox-testset-2.csv

@@ -0,0 +1,1001 @@
+seed, 0x0
+0, 0x399e5b222b82fa9
+1, 0x41fd08c1f00f3bc5
+2, 0x78b8824162ee4d04
+3, 0x176747919e02739d
+4, 0xfaa88f002a8d3596
+5, 0x418eb6f592e6c227
+6, 0xef83020b8344dd45
+7, 0x30a74a1a6eaa064b
+8, 0x93d43bf97a490c3
+9, 0xe4ba28b442194cc
+10, 0xc829083a168a8656
+11, 0x73f45d50f8e22849
+12, 0xf912db57352824cc
+13, 0xf524216927b12ada
+14, 0x22b7697473b1dfda
+15, 0x311e2a936414b39f
+16, 0xb905abfdcc425be6
+17, 0x4b14630d031eac9c
+18, 0x1cf0c4ae01222bc8
+19, 0xa6c33efc6e82ef3
+20, 0x43b3576937ba0948
+21, 0x1e483d17cdde108a
+22, 0x6722784cac11ac88
+23, 0xee87569a48fc45d7
+24, 0xb821dcbe74d18661
+25, 0xa5d1876ef3da1a81
+26, 0xe4121c2af72a483
+27, 0x2d747e355a52cf43
+28, 0x609059957bd03725
+29, 0xc3327244b49e16c5
+30, 0xb5ae6cb000dde769
+31, 0x774315003209017
+32, 0xa2013397ba8db605
+33, 0x73b228945dbcd957
+34, 0x801af7190375d3c0
+35, 0xae6dca29f24c9c67
+36, 0xd1cc0bcb1ca26249
+37, 0x1defa62a5bd853be
+38, 0x67c2f5557fa89462
+39, 0xf1729b58122fab02
+40, 0xb67eb71949ec6c42
+41, 0x5456366ec1f8f7d7
+42, 0x44492b32eb7966f5
+43, 0xa801804159f175f1
+44, 0x5a416f23cac70d84
+45, 0x186f55293302303d
+46, 0x7339d5d7b6a43639
+47, 0xfc6df38d6a566121
+48, 0xed2fe018f150b39e
+49, 0x508e0b04a781fa1b
+50, 0x8bee9d50f32eaf50
+51, 0x9870015d37e63cc
+52, 0x93c6b12309c14f2d
+53, 0xb571cf798abe93ff
+54, 0x85c35a297a88ae6e
+55, 0x9b1b79afe497a2ae
+56, 0x1ca02e5b95d96b8d
+57, 0x5bb695a666c0a94a
+58, 0x4e3caf9bbab0b208
+59, 0x44a44be1a89f2dc1
+60, 0x4ff37c33445758d1
+61, 0xd0e02875322f35da
+62, 0xfd449a91fb92646b
+63, 0xbe0b49096b95db4d
+64, 0xffa3647cad13ef5d
+65, 0x75c127a61acd10c8
+66, 0xd65f697756f5f98e
+67, 0x3ced84be93d94434
+68, 0x4da3095c2fc46d68
+69, 0x67564e2a771ee9ac
+70, 0x36944775180644a9
+71, 0xf458db1c177cdb60
+72, 0x5b58406dcd034c8
+73, 0x793301a3fdab2a73
+74, 0x1c2a1a16d6db6128
+75, 0xc2dacd4ddddbe56c
+76, 0x2e7d15be2301a111
+77, 0xd4f4a6341b3bcd18
+78, 0x3622996bbe6a9e3b
+79, 0xaf29aa9a7d6d47da
+80, 0x6d7dbb74a4cd68ae
+81, 0xc260a17e0f39f841
+82, 0xdee0170f2af66f0d
+83, 0xf84ae780d7b5a06e
+84, 0x8326247b73f43c3a
+85, 0xd44eef44b4f98b84
+86, 0x3d10aee62ec895e3
+87, 0x4f23fef01bf703b3
+88, 0xf8e50aa57d888df6
+89, 0x7da67411e3bef261
+90, 0x1d00f2769b2f96d7
+91, 0x7ef9a15b7444b84e
+92, 0xcfa16436cc2b7e21
+93, 0x29ab8cfac00460ff
+94, 0x23613de8608b0e70
+95, 0xb1aa0980625798a8
+96, 0xb9256fd29db7df99
+97, 0xdacf311bf3e7fa18
+98, 0xa013c8f9fada20d8
+99, 0xaf5fd4fe8230fe3e
+100, 0xd3d59ca55102bc5c
+101, 0x9d08e2aa5242767f
+102, 0x40278fe131e83b53
+103, 0x56397d03c7c14c98
+104, 0xe874b77b119359b3
+105, 0x926a1ba4304ab19f
+106, 0x1e115d5aa695a91d
+107, 0xc6a459df441f2fe3
+108, 0x2ca842bc1b0b3c6a
+109, 0x24c804cf8e5eed16
+110, 0x7ca00fc4a4c3ebd3
+111, 0x546af7cecc4a4ba6
+112, 0x8faae1fa18fd6e3
+113, 0x40420b0089641a6a
+114, 0x88175a35d9abcb83
+115, 0xf7d746d1b8b1357c
+116, 0x7dae771a651be970
+117, 0x2f6485247ee4df84
+118, 0x6883702fab2d8ec5
+119, 0xeb7eea829a67f9a6
+120, 0x60d5880b485562ed
+121, 0x7d4ca3d7e41a4e7e
+122, 0xbb7fef961ab8de18
+123, 0x3b92452fb810c164
+124, 0x5f4b4755348b338
+125, 0xca45a715a7539806
+126, 0xc33efd9da5399dd
+127, 0x593d665a51d4aedd
+128, 0x75d6b8636563036b
+129, 0x7b57caa55e262082
+130, 0x4ede7427969e0dd5
+131, 0xc3f19b6f78ea00b
+132, 0xeea7bab9be2181ea
+133, 0x652c45fe9c420c04
+134, 0x14ba9e3d175670ee
+135, 0xd2ad156ba6490474
+136, 0x4d65ae41065f614
+137, 0x6ff911c8afa28eb1
+138, 0xedc2b33588f3cb68
+139, 0x437c8bc324666a2f
+140, 0x828cee25457a3f0
+141, 0x530c986091f31b9b
+142, 0x2f34671e8326ade7
+143, 0x4f686a8f4d77f6da
+144, 0xa4c1987083498895
+145, 0xbce5a88b672b0fb1
+146, 0x8476115a9e6a00cc
+147, 0x16de18a55dd2c238
+148, 0xdf38cf4c416232bc
+149, 0x2cb837924e7559f3
+150, 0xfad4727484e982ed
+151, 0x32a55d4b7801e4f
+152, 0x8b9ef96804bd10a5
+153, 0xa1fd422c9b5cf2a9
+154, 0xf46ddb122eb7e442
+155, 0x6e3842547afa3b33
+156, 0x863dee1c34afe5c4
+157, 0x6a43a1935b6db171
+158, 0x1060a5c2f8145821
+159, 0xf783ec9ed34c4607
+160, 0x1da4a86bf5f8c0b0
+161, 0x4c7714041ba12af8
+162, 0x580da7010be2f192
+163, 0xad682fe795a7ea7a
+164, 0x6687b6cb88a9ed2c
+165, 0x3c8d4b175517cd18
+166, 0xe9247c3a524a6b6b
+167, 0x337ca9cfaa02658
+168, 0xed95399481c6feec
+169, 0x58726a088e606062
+170, 0xfe7588a5b4ee342a
+171, 0xee434c7ed146fdee
+172, 0xe2ade8b60fdc4ba5
+173, 0xd57e4c155de4eaab
+174, 0xdefeae12de1137cb
+175, 0xb7a276a241316ac1
+176, 0xeb838b1b1df4ca15
+177, 0x6f78965edea32f6f
+178, 0x18bebd264d7a5d53
+179, 0x3641c691d77005ec
+180, 0xbe70ed7efea8c24c
+181, 0x33047fa8d03ca560
+182, 0x3bed0d2221ff0f87
+183, 0x23083a6ffbcf38a2
+184, 0xc23eb827073d3fa5
+185, 0xc873bb3415e9fb9b
+186, 0xa4645179e54147fe
+187, 0x2c72fb443f66e207
+188, 0x98084915dd89d8f4
+189, 0x88baa2de12c99037
+190, 0x85c74ab238cb795f
+191, 0xe122186469ea3a26
+192, 0x4c3bba99b3249292
+193, 0x85d6845d9a015234
+194, 0x147ddd69c13e6a31
+195, 0x255f4d678c9a570b
+196, 0x2d7c0c410bf962b4
+197, 0x58eb7649e0aa16ca
+198, 0x9d240bf662fe0783
+199, 0x5f74f6fa32d293cc
+200, 0x4928e52f0f79d9b9
+201, 0xe61c2b87146b706d
+202, 0xcfcd90d100cf5431
+203, 0xf15ea8138e6aa178
+204, 0x6ab8287024f9a819
+205, 0xed8942593db74e01
+206, 0xefc00e4ec2ae36dd
+207, 0xc21429fb9387f334
+208, 0xf9a3389e285a9bce
+209, 0xacdee8c43aae49b3
+210, 0xefc382f02ad55c25
+211, 0x1153b50e8d406b72
+212, 0xb00d39ebcc2f89d8
+213, 0xde62f0b9831c8850
+214, 0xc076994662eef6c7
+215, 0x66f08f4752f1e3ef
+216, 0x283b90619796249a
+217, 0x4e4869bc4227499e
+218, 0xb45ad78a49efd7ed
+219, 0xffe19aa77abf5f4b
+220, 0xfce11a0daf913aef
+221, 0x7e4e64450d5cdceb
+222, 0xe9621997cfd62762
+223, 0x4d2c9e156868081
+224, 0x4e2d96eb7cc9a08
+225, 0xda74849bba6e3bd3
+226, 0x6f4621da935e7fde
+227, 0xb94b914aa0497259
+228, 0xd50d03e8b8db1563
+229, 0x1a45c1ce5dca422e
+230, 0xc8d30d33276f843f
+231, 0xb57245774e4176b4
+232, 0x8d36342c05abbbb1
+233, 0x3591ad893ecf9e78
+234, 0x62f4717239ee0ac8
+235, 0x9b71148a1a1d4200
+236, 0x65f8e0f56dd94463
+237, 0x453b1fcfd4fac8c2
+238, 0x4c25e48e54a55865
+239, 0xa866baa05112ace2
+240, 0x7741d3c69c6e79c5
+241, 0x7deb375e8f4f7a8a
+242, 0xc242087ede42abd8
+243, 0x2fa9d1d488750c4b
+244, 0xe8940137a935d3d3
+245, 0x1dab4918ca24b2f2
+246, 0xe2368c782168fe3e
+247, 0x6e8b2d1d73695909
+248, 0x70455ebea268b33e
+249, 0x656a919202e28da1
+250, 0x5a5a8935647da999
+251, 0x428c6f77e118c13c
+252, 0xa87aee2b675bb083
+253, 0x3873a6412b239969
+254, 0x5f72c1e91cb8a2ee
+255, 0xa25af80a1beb5679
+256, 0x1af65d27c7b4abc3
+257, 0x133437060670e067
+258, 0xb1990fa39a97d32e
+259, 0x724adc89ae10ed17
+260, 0x3f682a3f2363a240
+261, 0x29198f8dbd343499
+262, 0xdfaeeaa42bc51105
+263, 0x5baff3901b9480c2
+264, 0x3f760a67043e77f5
+265, 0x610fa7aa355a43ba
+266, 0x394856ac09c4f7a7
+267, 0x1d9229d058aee82e
+268, 0x19c674804c41aeec
+269, 0x74cf12372012f4aa
+270, 0xa5d89b353fa2f6ca
+271, 0x697e4f672ac363dd
+272, 0xde6f55ba73df5af9
+273, 0x679cf537510bd68f
+274, 0x3dc916114ae9ef7e
+275, 0xd7e31a66ec2ee7ba
+276, 0xc21bebb968728495
+277, 0xc5e0781414e2adfd
+278, 0x71147b5412ddd4bd
+279, 0x3b864b410625cca9
+280, 0x433d67c0036cdc6
+281, 0x48083afa0ae20b1b
+282, 0x2d80beecd64ac4e8
+283, 0x2a753c27c3a3ee3e
+284, 0xb2c5e6afd1fe051a
+285, 0xea677930cd66c46b
+286, 0x4c3960932f92810a
+287, 0xf1b367a9e527eaba
+288, 0xb7d92a8a9a69a98e
+289, 0x9f9ad3210bd6b453
+290, 0x817f2889db2dcbd8
+291, 0x4270a665ac15813c
+292, 0x90b85353bd2be4dd
+293, 0x10c0460f7b2d68d
+294, 0x11cef32b94f947f5
+295, 0x3cf29ed8e7d477e8
+296, 0x793aaa9bd50599ef
+297, 0xbac15d1190014aad
+298, 0x987944ae80b5cb13
+299, 0x460aa51f8d57c484
+300, 0xc77df0385f97c2d3
+301, 0x92e743b7293a3822
+302, 0xbc3458bcfbcbb8c0
+303, 0xe277bcf3d04b4ed7
+304, 0xa537ae5cf1c9a31c
+305, 0x95eb00d30bd8cfb2
+306, 0x6376361c24e4f2dd
+307, 0x374477fe87b9ea8e
+308, 0x8210f1a9a039902e
+309, 0xe7628f7031321f68
+310, 0x8b8e9c0888fc1d3d
+311, 0x306be461fdc9e0ed
+312, 0x510009372f9b56f5
+313, 0xa6e6fa486b7a027a
+314, 0x9d3f002025203b5a
+315, 0x7a46e0e81ecbef86
+316, 0x41e280c611d04df0
+317, 0xedcec10418a99e8a
+318, 0x5c27b6327e0b9dbd
+319, 0xa81ed2035b509f07
+320, 0x3581e855983a4cc4
+321, 0x4744594b25e9809d
+322, 0xc737ac7c27fbd0ed
+323, 0x1b523a307045433a
+324, 0x8b4ce9171076f1d9
+325, 0x2db02d817cd5eec0
+326, 0x24a1f1229af50288
+327, 0x5550c0dcf583ff16
+328, 0x3587baaa122ec422
+329, 0xf9d3dc894229e510
+330, 0xf3100430d5cf8e87
+331, 0xc31af79862f8e2fb
+332, 0xd20582063b9f3537
+333, 0xac5e90ac95fcc7ad
+334, 0x107c4c704d5109d4
+335, 0xebc8628906dbfd70
+336, 0x215242776da8c531
+337, 0xa98002f1dcf08b51
+338, 0xbc3bdc07f3b09718
+339, 0x238677062495b512
+340, 0x53b4796f2a3c49e8
+341, 0x6424286467e22f0e
+342, 0x14d0952a11a71bac
+343, 0x2f97098149b82514
+344, 0x3777f2fdc425ad2
+345, 0xa32f2382938876d4
+346, 0xda8a39a021f20ae3
+347, 0x364361ef0a6ac32c
+348, 0x4413eede008ff05a
+349, 0x8dda8ace851aa327
+350, 0x4303cabbdcecd1ee
+351, 0x2e69f06d74aa549f
+352, 0x4797079cd4d9275c
+353, 0xc7b1890917e98307
+354, 0x34031b0e822a4b4c
+355, 0xfc79f76b566303ea
+356, 0x77014adbe255a930
+357, 0xab6c43dd162f3be5
+358, 0xa430041f3463f6b9
+359, 0x5c191a32ada3f84a
+360, 0xe8674a0781645a31
+361, 0x3a11cb667b8d0916
+362, 0xaedc73e80c39fd8a
+363, 0xfde12c1b42328765
+364, 0x97abb7dcccdc1a0b
+365, 0x52475c14d2167bc8
+366, 0x540e8811196d5aff
+367, 0xa867e4ccdb2b4b77
+368, 0x2be04af61e5bcfb9
+369, 0x81b645102bfc5dfd
+370, 0x96a52c9a66c6450f
+371, 0x632ec2d136889234
+372, 0x4ed530c0b36a6c25
+373, 0x6f4851225546b75
+374, 0x2c065d6ba46a1144
+375, 0xf8a3613ff416551d
+376, 0xb5f0fd60e9c971a9
+377, 0x339011a03bb4be65
+378, 0x9439f72b6995ded6
+379, 0xc1b03f3ef3b2292d
+380, 0xad12fd221daab3ae
+381, 0xf615b770f2cf996f
+382, 0x269d0fdcb764172
+383, 0x67837025e8039256
+384, 0x6402831fc823fafa
+385, 0x22854146a4abb964
+386, 0x7b5ad9b5a1bad7a8
+387, 0x67170e7beb6ac935
+388, 0xfc2d1e8e24adfaaa
+389, 0x7ded4395345ff40d
+390, 0x418981760a80dd07
+391, 0xc03bef38022c1d2
+392, 0x3a11850b26eade29
+393, 0xaa56d02c7175c5f4
+394, 0xd83b7917b9bfbff5
+395, 0x3c1df2f8fa6fced3
+396, 0xf3d6e2999c0bb760
+397, 0xc66d683a59a950e3
+398, 0x8e3972a9d73ffabf
+399, 0x97720a0443edffd9
+400, 0xa85f5d2fe198444a
+401, 0xfc5f0458e1b0de5e
+402, 0xe3973f03df632b87
+403, 0xe151073c84c594b3
+404, 0x68eb4e22e7ff8ecf
+405, 0x274f36eaed7cae27
+406, 0x3b87b1eb60896b13
+407, 0xbe0b2f831442d70a
+408, 0x2782ed7a48a1b328
+409, 0xb3619d890310f704
+410, 0xb03926b11b55921a
+411, 0xdb46fc44aa6a0ce4
+412, 0x4b063e2ef2e9453a
+413, 0xe1584f1aeec60fb5
+414, 0x7092bd6a879c5a49
+415, 0xb84e1e7c7d52b0e6
+416, 0x29d09ca48db64dfb
+417, 0x8f6c4a402066e905
+418, 0x77390795eabc36b
+419, 0xcc2dc2e4141cc69f
+420, 0x2727f83beb9e3c7c
+421, 0x1b29868619331de0
+422, 0xd38c571e192c246f
+423, 0x535327479fe37b6f
+424, 0xaff9ce5758617eb3
+425, 0x5658539e9288a4e4
+426, 0x8df91d87126c4c6d
+427, 0xe931cf8fdba6e255
+428, 0x815dfdf25fbee9e8
+429, 0x5c61f4c7cba91697
+430, 0xdd5f5512fe2313a1
+431, 0x499dd918a92a53cd
+432, 0xa7e969d007c97dfd
+433, 0xb8d39c6fc81ac0bb
+434, 0x1d646983def5746c
+435, 0x44d4b3b17432a60c
+436, 0x65664232a14db1e3
+437, 0xda8fae6433e7500b
+438, 0xbe51b94ff2a3fe94
+439, 0xe9b1bd9a9098ef9f
+440, 0xfe47d54176297ef5
+441, 0xb8ab99bc03bb7135
+442, 0xcfad97f608565b38
+443, 0xf05da71f6760d9c1
+444, 0xef8da40a7c70e7b
+445, 0xe0465d58dbd5d138
+446, 0xb54a2d70eb1a938
+447, 0xfdd50c905958f2d8
+448, 0x3c41933c90a57d43
+449, 0x678f6d894c6ad0bb
+450, 0x403e8f4582274e8
+451, 0x5cbbe975668df6b0
+452, 0x297e6520a7902f03
+453, 0x8f6dded33cd1efd7
+454, 0x8e903c97be8d783b
+455, 0x10bd015577e30f77
+456, 0x3fcd69d1c36eab0c
+457, 0xb45989f3ca198d3
+458, 0x507655ce02b491a9
+459, 0xa92cf99bb78602ce
+460, 0xebfb82055fbc2f0f
+461, 0x3334256279289b7a
+462, 0xc19d2a0f740ee0ac
+463, 0x8bb070dea3934905
+464, 0xa4ab57d3a8d1b3eb
+465, 0xfee1b09bcacf7ff4
+466, 0xccc7fb41ceec41fa
+467, 0xd4da49094eb5a74d
+468, 0xed5c693770af02ed
+469, 0x369dabc9bbfaa8e4
+470, 0x7eab9f360d054199
+471, 0xe36dbebf5ee94076
+472, 0xd30840e499b23d7
+473, 0x8678e6cb545015ff
+474, 0x3a47932ca0b336e
+475, 0xeb7c742b6e93d6fe
+476, 0x1404ea51fe5a62a9
+477, 0xa72cd49db978e288
+478, 0xfd7bada020173dcf
+479, 0xc9e74fc7abe50054
+480, 0x93197847bb66808d
+481, 0x25fd5f053dce5698
+482, 0xe198a9b18cc21f4
+483, 0x5cc27b1689452d5d
+484, 0x8b3657af955a98dc
+485, 0xc17f7584f54aa1c0
+486, 0xe821b088246b1427
+487, 0x32b5a9f6b45b6fa0
+488, 0x2aef7c315c2bae0c
+489, 0xe1af8129846b705a
+490, 0x4123b4c091b34614
+491, 0x6999d61ec341c073
+492, 0x14b9a8fcf86831ea
+493, 0xfd4cff6548f46c9f
+494, 0x350c3b7e6cc8d7d6
+495, 0x202a5047fecafcd5
+496, 0xa82509fe496bb57d
+497, 0x835e4b2608b575fe
+498, 0xf3abe3da919f54ec
+499, 0x8705a21e2c9b8796
+500, 0xfd02d1427005c314
+501, 0xa38458faa637f49b
+502, 0x61622f2360e7622a
+503, 0xe89335a773c2963b
+504, 0x481264b659b0e0d0
+505, 0x1e82ae94ebf62f15
+506, 0x8ea7812de49209d4
+507, 0xff963d764680584
+508, 0x418a68bef717f4af
+509, 0x581f0e7621a8ab91
+510, 0x840337e9a0ec4150
+511, 0x951ef61b344be505
+512, 0xc8b1b899feb61ec2
+513, 0x8b78ca13c56f6ed9
+514, 0x3d2fd793715a946f
+515, 0xf1c04fabcd0f4084
+516, 0x92b602614a9a9fcc
+517, 0x7991bd7a94a65be7
+518, 0x5dead10b06cad2d7
+519, 0xda7719b33f722f06
+520, 0x9d87a722b7bff71e
+521, 0xb038e479071409e9
+522, 0xf4e8bbec48054775
+523, 0x4fec2cd7a28a88ea
+524, 0x839e28526aad3e56
+525, 0xd37ec57852a98bf0
+526, 0xdef2cbbe00f3a02d
+527, 0x1aecfe01a9e4d801
+528, 0x59018d3c8beaf067
+529, 0x892753e6ac8bf3cd
+530, 0xefdd3437023d2d1c
+531, 0x447bfbd148c8cb88
+532, 0x282380221bd442b8
+533, 0xfce8658d1347384a
+534, 0x60b211a7ec6bfa8
+535, 0xd21729cfcc692974
+536, 0x162087ecd5038a47
+537, 0x2b17000c4bce39d2
+538, 0x3a1f75ff6adcdce0
+539, 0x721a411d312f1a2c
+540, 0x9c13b6133f66934d
+541, 0xaa975d14978980e5
+542, 0x9403dbd4754203fa
+543, 0x588c15762fdd643
+544, 0xdd1290f8d0ada73a
+545, 0xd9b77380936103f4
+546, 0xb2e2047a356eb829
+547, 0x7019e5e7f76f7a47
+548, 0x3c29a461f62b001d
+549, 0xa07dc6cfab59c116
+550, 0x9b97e278433f8eb
+551, 0x6affc714e7236588
+552, 0x36170aeb32911a73
+553, 0x4a665104d364a789
+554, 0x4be01464ec276c9c
+555, 0x71bb10271a8b4ecf
+556, 0xbf62e1d068bc018
+557, 0xc9ada5db2cbbb413
+558, 0x2bded75e726650e5
+559, 0x33d5a7af2f34385d
+560, 0x8179c46661d85657
+561, 0x324ebcfd29267359
+562, 0xac4c9311dc9f9110
+563, 0xc14bb6a52f9f9c0
+564, 0xc430abe15e7fb9db
+565, 0xf1cce5c14df91c38
+566, 0x651e3efa2c0750d3
+567, 0x38a33604a8be5c75
+568, 0x7aaf77fe7ff56a49
+569, 0xc0d1cc56bbf27706
+570, 0x887aa47324e156c6
+571, 0x12547c004b085e8d
+572, 0xd86a8d6fbbbfd011
+573, 0x57c860188c92d7b4
+574, 0xcd5d3843d361b8ca
+575, 0x8f586ef05a9cb3ef
+576, 0x174456e1ba6267d5
+577, 0xf5dc302c62fe583c
+578, 0xa349442fabcdb71
+579, 0xe5123c1a8b6fd08e
+580, 0x80681552aa318593
+581, 0xb295396deaef1e31
+582, 0xabb626e0b900e32b
+583, 0xf024db8d3f19c15e
+584, 0x1d04bb9548e2fb6c
+585, 0xd8ed2b2214936c2b
+586, 0x618ca1e430a52bc9
+587, 0xccbca44a6088136b
+588, 0xd0481855c8b9ccbe
+589, 0x3c92a2fade28bdf7
+590, 0x855e9fefc38c0816
+591, 0x1269bbfe55a7b27c
+592, 0x1d6c853d83726d43
+593, 0xc8655511cc7fcafc
+594, 0x301503eb125a9b0e
+595, 0xb3108e4532016b11
+596, 0xbb7ab6245da9cb3d
+597, 0x18004c49116d85eb
+598, 0x3480849c20f61129
+599, 0xe28f45157463937b
+600, 0x8e85e61060f2ce1
+601, 0x1673da4ec589ba5e
+602, 0x74b9a6bd1b194712
+603, 0xed39e147fa8b7601
+604, 0x28ce54019102ca77
+605, 0x42e0347f6d7a2f30
+606, 0xb6a908d1c4814731
+607, 0x16c3435e4e9a126d
+608, 0x8880190514c1ad54
+609, 0xfffd86229a6f773c
+610, 0x4f2420cdb0aa1a93
+611, 0xf8e1acb4120fc1fa
+612, 0x63a8c553ab36a2f2
+613, 0x86b88cf3c0a6a190
+614, 0x44d8b2801622c792
+615, 0xf6eae14e93082ff1
+616, 0xd9ed4f5d1b8fac61
+617, 0x1808ce17f4e1f70
+618, 0x446e83ea336f262f
+619, 0xc7c802b04c0917b7
+620, 0x626f45fd64968b73
+621, 0x9ffa540edc9b2c5c
+622, 0xa96a1e219e486af8
+623, 0x2bb8963884e887a1
+624, 0xba7f68a5d029e3c4
+625, 0xefc45f44392d9ca0
+626, 0x98d77762503c5eab
+627, 0xd89bcf62f2da627c
+628, 0xa3cab8347f833151
+629, 0xa095b7595907d5c7
+630, 0x3b3041274286181
+631, 0xb518db8919eb71fa
+632, 0x187036c14fdc9a36
+633, 0xd06e28301e696f5d
+634, 0xdbc71184e0c56492
+635, 0xfe51e9cae6125bfd
+636, 0x3b12d17cd014df24
+637, 0x3b95e4e2c986ac1a
+638, 0x29c1cce59fb2dea2
+639, 0x58c05793182a49d6
+640, 0xc016477e330d8c00
+641, 0x79ef335133ada5d
+642, 0x168e2cad941203f3
+643, 0xf99d0f219d702ef0
+644, 0x655628068f8f135b
+645, 0xdcdea51910ae3f92
+646, 0x8e4505039c567892
+647, 0x91a9ec7e947c89ae
+648, 0x8717172530f93949
+649, 0x1c80aba9a440171a
+650, 0x9c8f83f6ebe7441e
+651, 0x6c05e1efea4aa7f9
+652, 0x10af696b777c01b
+653, 0x5892e9d9a92fc309
+654, 0xd2ba7da71e709432
+655, 0x46378c7c3269a466
+656, 0x942c63dfe18e772c
+657, 0x6245cf02ef2476f
+658, 0x6f265b2759ea2aea
+659, 0x5aa757f17d17f4a6
+660, 0x1ad6a3c44fa09be6
+661, 0xe861af14e7015fb8
+662, 0x86be2e7db388c77
+663, 0x5c7bba32b519e9a0
+664, 0x3feb314850c4437b
+665, 0x97955add60cfb45b
+666, 0xfdb536230a540bdc
+667, 0xdac9d7bf6e58512e
+668, 0x4894c00e474e8120
+669, 0xa1918a37739da366
+670, 0xa8097f2096532807
+671, 0x592afe50e6c5e643
+672, 0xd69050ee6dcb33dc
+673, 0xa6956b262dd3c561
+674, 0x1a55c815555e63f7
+675, 0x2ec7fd37516de2bb
+676, 0x8ec251d9c70e76ba
+677, 0x9b76e4abafd2689
+678, 0x9ce3f5c751a57df1
+679, 0x915c4818bf287bc7
+680, 0x2293a0d1fe07c735
+681, 0x7627dcd5d5a66d3d
+682, 0xb5e4f92cc49c7138
+683, 0x6fc51298731d268c
+684, 0xd19800aa95441f87
+685, 0x14f70f31162fa115
+686, 0x41a3da3752936f59
+687, 0xbec0652be95652ee
+688, 0x7aa4bdb1020a290f
+689, 0x4382d0d9bee899ef
+690, 0xe6d988ae4277d6ff
+691, 0xe618088ccb2a32d1
+692, 0x411669dfaa899e90
+693, 0x234e2bf4ba76d9f
+694, 0xe109fe4cb7828687
+695, 0x1fb96b5022b0b360
+696, 0x6b24ad76c061a716
+697, 0x7e1781d4d7ecee15
+698, 0xf20c2dbe82ba38ba
+699, 0xeda8e8ae1d943655
+700, 0xa58d196e2a77eaec
+701, 0x44564765a5995a0b
+702, 0x11902fe871ecae21
+703, 0x2ea60279900e675d
+704, 0x38427227c18a9a96
+705, 0xe0af01490a1b1b48
+706, 0x826f91997e057824
+707, 0x1e57308e6e50451
+708, 0xb42d469bbbfdc350
+709, 0xb9734cff1109c49b
+710, 0x98967559bb9d364f
+711, 0xd6be360041907c12
+712, 0xa86a1279122a1e21
+713, 0x26f99a8527bfc698
+714, 0xfa8b85758f28f5d6
+715, 0xe3057429940806ae
+716, 0x4bee2d7e84f93b2b
+717, 0x948350a76ea506f4
+718, 0xa139154488045e74
+719, 0x8893579ba5e78085
+720, 0x5f21c215c6a9e397
+721, 0x456134f3a59641dc
+722, 0x92c0273f8e97a9c6
+723, 0xd2936c9c3f0c6936
+724, 0xcfa4221e752c4735
+725, 0x28cd5a7457355dca
+726, 0xecdfdde23d90999f
+727, 0x60631b2d494d032b
+728, 0xf67289df269a827f
+729, 0xcbe8011ef0f5b7ef
+730, 0x20eea973c70a84f5
+731, 0xbe1fd200398557ce
+732, 0xd2279ee030191bba
+733, 0xf2bd4291dedaf819
+734, 0xfc6d167dbe8c402
+735, 0x39ac298da5d0044b
+736, 0xceac026f5f561ce
+737, 0x10a5b0bdd8ad60e6
+738, 0xdeb3c626df6d4bcb
+739, 0x3c128962e77ff6ca
+740, 0xc786262e9c67a0e5
+741, 0x4332855b3febcdc0
+742, 0x7bda9724d1c0e020
+743, 0x6a8c93399bc4df22
+744, 0xa9b20100ac707396
+745, 0xa11a3458502c4eb5
+746, 0xb185461c60478941
+747, 0x13131d56195b7ff6
+748, 0x8d55875ddbd4aa1c
+749, 0xc09b67425f469aa5
+750, 0x39e33786cc7594c4
+751, 0x75e96db8e4b08b93
+752, 0xda01cd12a3275d1e
+753, 0x2c49e7822344fab5
+754, 0x9bd5f10612514ca7
+755, 0x1c801a5c828e7332
+756, 0x29797d3f4f6c7b4c
+757, 0xac992715e21e4e53
+758, 0xe40e89ee887ddb37
+759, 0x15189a2b265a783b
+760, 0xa854159a52af5c5
+761, 0xb9d8a5a81c12bead
+762, 0x3240cdc9d59e2a58
+763, 0x1d0b872234cf8e23
+764, 0xc01224cf6ce12cff
+765, 0x2601e9f3905c8663
+766, 0xd4ecf9890168d6b4
+767, 0xa45db796d89bfdd5
+768, 0x9f389406dad64ab4
+769, 0xa5a851adce43ffe3
+770, 0xd0962c41c26e5aa9
+771, 0x8a671679e48510a4
+772, 0xc196dc0924a6bfeb
+773, 0x3ead661043b549cb
+774, 0x51af4ca737d405ac
+775, 0xf4425b5c62275fb6
+776, 0x71e69d1f818c10f5
+777, 0xacaf4af2d3c70162
+778, 0x2e1f1d4fd7524244
+779, 0xe54fdd8f388890e8
+780, 0xfda0d33e84eb2b83
+781, 0x53965c5e392b81da
+782, 0x5c92288267263097
+783, 0xcac1b431c878c66c
+784, 0x36c0e1cf417241c6
+785, 0x5cc4d9cd1a36bf2c
+786, 0x32e4257bb5d3e470
+787, 0x4aecff904adb44fb
+788, 0x4d91a8e0d1d60cac
+789, 0xa3b478388385b038
+790, 0x48d955f24eba70be
+791, 0x310e4deb07f24f68
+792, 0x8853e73b1f30a5a
+793, 0x278aee45c2a65c5
+794, 0xf6932eedbd62fb0b
+795, 0xafb95958c82fafad
+796, 0x78e807c18616c16c
+797, 0xd7abadda7488ed9f
+798, 0x2dd72e2572aa2ae6
+799, 0x6ec3791982c2be09
+800, 0x6865bb314fac478f
+801, 0xa14dc0ce09000d1a
+802, 0xb8081ad134da10f2
+803, 0xc4ac1534aa825ef5
+804, 0xd83aeb48ae2d538f
+805, 0x38052027e3074be4
+806, 0xa9833e06ef136582
+807, 0x4f02d790ec9fd78
+808, 0xec2f60bc711c5bdc
+809, 0x9253b0d12268e561
+810, 0xa8ac607fdd62c206
+811, 0x895e28ebc920289f
+812, 0xe2fd42b154243ac7
+813, 0xc69cac2f776eee19
+814, 0xf4d4ac11db56d0dc
+815, 0xa8d37049b9f39833
+816, 0x75abbf8a196c337c
+817, 0xb115bb76750d27b8
+818, 0x39426d187839154
+819, 0xd488423e7f38bf83
+820, 0xbb92e0c76ecb6a62
+821, 0x3055a018ce39f4e3
+822, 0xc93fe0e907729bfb
+823, 0x65985d17c5863340
+824, 0x2088ae081b2028e1
+825, 0x6e628de873314057
+826, 0x864377cccf573f0e
+827, 0xae03f4c9aa63d132
+828, 0xb1db766d6404c66d
+829, 0xdce5a22414a374b
+830, 0x622155b777819997
+831, 0x69fe96e620371f3c
+832, 0xa9c67dbc326d94fc
+833, 0x932a84ae5dd43bab
+834, 0xe2301a20f6c48c3f
+835, 0x795d2e79c6477300
+836, 0xd8e3e631289521e7
+837, 0xae2684979002dfd6
+838, 0xc9c2392377550f89
+839, 0xa1b0c99d508ef7ec
+840, 0x593aef3c5a5272ec
+841, 0xe32e511a4b7162cd
+842, 0xab3b81655f5a2857
+843, 0x1b535e1a0aaf053e
+844, 0x5b33f56c1b6a07e2
+845, 0x782dc8cfcac4ef36
+846, 0xb3d4f256eecfd202
+847, 0xf73a6598f58c4f7e
+848, 0xd5722189524870ae
+849, 0x707878de6b995fc0
+850, 0xc3eb6ba73e3d7e8a
+851, 0xca75c017655b75a7
+852, 0x1b29369ea3541e5f
+853, 0x352e98858bdb58a3
+854, 0x1e4412d184b6b27d
+855, 0x2d375ba0304b2d17
+856, 0x56c30fce69a5d08e
+857, 0x6b8c2b0c06584bda
+858, 0xde4dfff228c8c91f
+859, 0xb7c9edd574e6287f
+860, 0xf6078281c9fca2b2
+861, 0xb9b9a51de02a2f1e
+862, 0xa411bef31c0103b0
+863, 0xc5facd8fc5e1d7a3
+864, 0x54e631c05ddf7359
+865, 0x815b42b3fd06c474
+866, 0xc9ac07566fda18ec
+867, 0xd84ea62957bd8e15
+868, 0x5575f74b5cfd8803
+869, 0x5779a8d460c2e304
+870, 0xfd6e87e264a85587
+871, 0xa1d674daa320b26d
+872, 0x2c3c3ec64b35afc4
+873, 0x393a274ff03e6935
+874, 0x1f40ecbac52c50ea
+875, 0xc3de64fa324ffc0c
+876, 0x56ae828b7f9deb04
+877, 0xe7c1a77b5c1f2cb3
+878, 0xa4c4aab19ea921cc
+879, 0xec164c238825822c
+880, 0xa6a3304770c03b03
+881, 0x3a63641d5b1e8123
+882, 0x42677be3a54617ef
+883, 0xa2680423e3a200c0
+884, 0x8b17cf75f3f37277
+885, 0xe7ce65a49242be3d
+886, 0x7f85934271323e4b
+887, 0xcfb0f431f79a4fab
+888, 0x392e4041a8505b65
+889, 0xd3e5daf0d8b25ea6
+890, 0x9447eff675d80f53
+891, 0xea27a9d53cfaeea8
+892, 0xe3f2335945a83ba
+893, 0x8875a43ce216413b
+894, 0xe49941f9eabce33e
+895, 0x9357c1296683a5b1
+896, 0xf0f16439e81ee701
+897, 0x3181515295ffd79a
+898, 0x9d7150fffd169ed8
+899, 0x2d6a1d281e255a72
+900, 0x81bf1286fb3a92b6
+901, 0x566d3079b499e279
+902, 0xc7939ca8f047341
+903, 0xb1f8050e7c2d59f6
+904, 0x605701045e7be192
+905, 0x51b73360e8e31a1c
+906, 0x9f4ad54483ba9fe0
+907, 0xd3085b8fcf69d1c8
+908, 0xc3e7475026dc5f0b
+909, 0x5800f8554b157354
+910, 0x37dfdf858cfcd963
+911, 0x3a1fce05ce385072
+912, 0xf495c062645c20c3
+913, 0xdcbeec2c3492c773
+914, 0xc38f427589d1d0b4
+915, 0x681ead60216a8184
+916, 0x4bd569c40cc88c41
+917, 0x49b0d442e130b7a2
+918, 0xee349156b7d1fa3f
+919, 0x2bde2d2db055135b
+920, 0xc6a460d2fbcb2378
+921, 0xd0f170494ff3dbb
+922, 0xb294422492528a23
+923, 0xfc95873c854e7b86
+924, 0x6c9c3ad1797bb19c
+925, 0xe0c06f2aab65062d
+926, 0x58e32ce0f11e3a81
+927, 0xa745fcd729ff5036
+928, 0x599b249b2fc2cdb2
+929, 0x78f23b5b0dd5b082
+930, 0x6de3e957f549ecfc
+931, 0x9d0712fa6d878756
+932, 0x9076e8554e4a413a
+933, 0xf3185818c0294de8
+934, 0x5de7cdf4b455b9b6
+935, 0xb15f6908ed703f7d
+936, 0x98c654dfedc6818
+937, 0x120502ab0e93ae42
+938, 0x67966a98a58dc120
+939, 0x1caa0fc628989482
+940, 0xd8b2c3cd480a8625
+941, 0x85c70071b3aed671
+942, 0xff385f8473714662
+943, 0xe2868e4bf3773b63
+944, 0x96cf8019b279298e
+945, 0x8511cc930bd74800
+946, 0x5312e48fdd55f5ab
+947, 0xfcdae564b52df78d
+948, 0x9eee48373e652176
+949, 0x953788f6bcbc56b0
+950, 0xd1a3855dbd2f6b37
+951, 0x3ad32acf77f4d1e9
+952, 0x917c7be81b003e30
+953, 0x9ce817da1e2e9dfb
+954, 0x2968983db162d44d
+955, 0x1e005decef5828ad
+956, 0xc38fe59d1aa4f3d5
+957, 0xf357f1710dc02f1d
+958, 0x2613912a4c83ec67
+959, 0x832a11470b9a17cb
+960, 0x5e85508a611f0dad
+961, 0x2781131677f59d56
+962, 0xa82358d7d4b0237f
+963, 0xfbf8b3cc030c3af6
+964, 0x68b2f68ac8a55adb
+965, 0x3b6fcf353add0ada
+966, 0xd1956049bcd15bd5
+967, 0x95b76f31c7f98b6d
+968, 0x814b6690df971a84
+969, 0xdcf7959cddd819e4
+970, 0xcf8c72c5d804fc88
+971, 0x56883769c8945a22
+972, 0x1f034652f658cf46
+973, 0x41df1324cda235a1
+974, 0xeccd32524504a054
+975, 0x974e0910a04ec02c
+976, 0x72104507b821f6db
+977, 0x791f8d089f273044
+978, 0xe0f79a4f567f73c3
+979, 0x52fe5bea3997f024
+980, 0x5f8b9b446494f78
+981, 0xfd9f511947059190
+982, 0x3aea9dac6063bce3
+983, 0xbfdae4dfc24aee60
+984, 0xa82cdbbf0a280318
+985, 0xf460aae18d70aa9d
+986, 0x997367cb204a57c4
+987, 0x616e21ab95ba05ef
+988, 0x9bfc93bec116769f
+989, 0x2b2ee27c37a3fa5b
+990, 0xb25c6ed54006ee38
+991, 0xab04d4a5c69e69a5
+992, 0x6d2f6b45f2d8438f
+993, 0x4ad2f32afc82f092
+994, 0x513d718908f709c0
+995, 0x5272aadc4fffca51
+996, 0xeb3f87e66156ef5d
+997, 0xf8a3d5a46a86ba85
+998, 0xdb4548a86f27abfd
+999, 0x57c05f47ff62380d

parrot/lib/python3.10/site-packages/numpy/random/tests/data/sfc64-testset-2.csv

@@ -0,0 +1,1001 @@
+seed, 0x0
+0, 0x91959e5fb96a6332
+1, 0x3c1dd8a25a7e9f21
+2, 0x657bdffc99798d9e
+3, 0x1a04de320b19e022
+4, 0x65b92af0e5f3c61c
+5, 0x9c84070ce8f743c0
+6, 0xbb10e573693cdb25
+7, 0xd65ea9e76b37fb6b
+8, 0x503efd0e76c8ae66
+9, 0xd711dcd04c26d0f
+10, 0x12f53f435814ac8c
+11, 0xb392cd402cfc82bd
+12, 0x461764550e06c889
+13, 0x716a48b3514e6979
+14, 0xdd0a322213c18ad7
+15, 0x6673a8ca0a05c4d7
+16, 0x2992ef333437f844
+17, 0xc4aaf7e8240b2aad
+18, 0x6ab0a1af1f41474f
+19, 0xb0bae400c226941d
+20, 0xe5f80c2eeeab48c6
+21, 0x3832c6a93a4024bf
+22, 0x280bd824fabe8368
+23, 0x66b626228321e5ff
+24, 0xe0bdfba5325a307e
+25, 0x3a5f65c6ef254e05
+26, 0x99ea12503cb02f94
+27, 0x5d01fd2db77d420b
+28, 0x6959bf5f36b2368d
+29, 0xd856e30c62b5f5be
+30, 0xe33233e1d8140e66
+31, 0xb78be619d415fa8d
+32, 0x4f943bb2cc63d3b
+33, 0x9b1460b290952d81
+34, 0x19205d794826740e
+35, 0x64617bd9d7a6a1ff
+36, 0x30442124b55ea76a
+37, 0xebbbc3b29d0333fc
+38, 0x39235a0fe359751c
+39, 0xf9629768891121aa
+40, 0x32052f53f366e05a
+41, 0x60cc5b412c925bc8
+42, 0xf8b7ecda1c0e5a9
+43, 0x195f036e170a2568
+44, 0xfe06d0381a9ca782
+45, 0x919d89e8b88eebbf
+46, 0xa47fb30148cf0d43
+47, 0x5c983e99d5f9fd56
+48, 0xe7492cdb6a1d42cd
+49, 0xf9cfe5c865b0cfd8
+50, 0x35b653367bbc3b99
+51, 0xb1d92f6f4d4e440b
+52, 0x737e1d5bd87ed9c0
+53, 0x7a880ca1498f8e17
+54, 0x687dae8494f9a3f7
+55, 0x6bae1989f441d5d7
+56, 0x71ad3fa5a9195c2e
+57, 0x16b3969779f5d03
+58, 0xd1bce2ac973f15b3
+59, 0xa114b1ee2ce0dcdd
+60, 0x270d75c11eb1b8d5
+61, 0xc48ffa087c0a7bc
+62, 0xaaf9dc48cda9848d
+63, 0x8111cf10ef6e584d
+64, 0x6736df6af40ee6f4
+65, 0x1a1a111682fbf98d
+66, 0xeb217658e1cb3b5d
+67, 0xcaf58a8b79de9dec
+68, 0x25d0ffd63c88d7a1
+69, 0x4c498cd871b7f176
+70, 0x4069a6156eb0cf3c
+71, 0xdf012f12edcdd867
+72, 0x7734c0ac8edb1689
+73, 0xed6960ac53dbc245
+74, 0x305e20da8868c661
+75, 0x5f0c7a3719956f95
+76, 0x66842bbe3b28895
+77, 0xb608bc9a31eac410
+78, 0xfcb17d5529503abd
+79, 0x829ae5cbc29b92ee
+80, 0x17f2f0027bc24f3a
+81, 0x435926c33d8f44cc
+82, 0x3ab899327098dbec
+83, 0xaf78573b27f8ead8
+84, 0xa8b334fabcf8dc60
+85, 0xcdf3b366a6a303db
+86, 0x8da9379dd62b34c8
+87, 0xb0ba511955f264a7
+88, 0x9d72e21a644f961d
+89, 0xfac28382e2e7e710
+90, 0xd457065f048410aa
+91, 0x1cae57d952563969
+92, 0x5a160a6223253e03
+93, 0x2c45df736d73c8bd
+94, 0x7f651ebc6ad9cec5
+95, 0x77a6be96c7d2e7e7
+96, 0x1721fb1dbfd6546a
+97, 0xf73f433ecff3c997
+98, 0xed1e80f680965bfe
+99, 0x6705ad67a3003b30
+100, 0xac21134efcadb9f7
+101, 0x4d2ba0a91d456ac
+102, 0x59da7b59434eb52b
+103, 0x26c1d070fd414b5f
+104, 0xed7079ddfce83d9a
+105, 0x9277d21f88e0fb7a
+106, 0xfae16b9a8d53d282
+107, 0xb08a0e2e405fdf7d
+108, 0x2ea20df44229d6ec
+109, 0x80e4634cd3612825
+110, 0xbe62e8aeba8f8a1a
+111, 0x4981209769c190fb
+112, 0xcec96ef14c7e1f65
+113, 0x73fe4457b47e7b53
+114, 0x1d66300677315c31
+115, 0xe26821290498c4cc
+116, 0xf6110248fd8fb1c5
+117, 0x30fd7fe32dbd8be3
+118, 0x534ec9b910a2bd72
+119, 0x8f9bfe878bbf7382
+120, 0x4f4eb5295c0c2193
+121, 0xdeb22f03a913be9e
+122, 0x40f716f8e2a8886c
+123, 0xc65007d0e386cdb1
+124, 0x9bdd26d92b143a14
+125, 0xf644b0b77ea44625
+126, 0x75f5a53f6b01993a
+127, 0xfe803e347bf41010
+128, 0x594bff5fa17bc360
+129, 0x3551edfb450373c7
+130, 0x898f9dad433615db
+131, 0x923d2406daa26d49
+132, 0x99e07faccbc33426
+133, 0x7389f9ff4470f807
+134, 0xdc2a25957c6df90b
+135, 0x33c6d8965ef3053f
+136, 0x51a8f07e838f1ab
+137, 0x91c5db369380274f
+138, 0xc37de65ac56b207e
+139, 0xfcc6d2375dde7f14
+140, 0xa4e6418bff505958
+141, 0x4b8b9f78e46953c4
+142, 0x255ab2e0f93cf278
+143, 0xdf650717af3d96ef
+144, 0x2caa21cba3aae2b2
+145, 0xce7e46c6f393daa4
+146, 0x1d5b3573f9997ac7
+147, 0x5280c556e850847d
+148, 0x32edc31bef920ad7
+149, 0xefaa6b0b08cf2c6
+150, 0x5151c99d97b111c5
+151, 0x35ccf4bf53d17590
+152, 0xa210d7bd8697b385
+153, 0xa9419f95738fbe61
+154, 0xdeccf93a1a4fdc90
+155, 0xd0ea3365b18e7a05
+156, 0x84122df6dcd31b9a
+157, 0x33040a2125cea5f5
+158, 0xfe18306a862f6d86
+159, 0xdb97c8392e5c4457
+160, 0xc3e0fa735e80e422
+161, 0x7d106ff36467a0c1
+162, 0xb9825eecc720a76d
+163, 0x7fefc6f771647081
+164, 0xf5df3f5b3977bf13
+165, 0x18fb22736d36f1e0
+166, 0xadc4637b4953abfc
+167, 0x174e66d3e17974bd
+168, 0xf1614c51df4db5db
+169, 0x6664ecde5717b293
+170, 0xd5bc5b6839265c26
+171, 0xf6ca9ce1af3f1832
+172, 0xca696789a9d506ea
+173, 0x7399c246c8f9d53
+174, 0xadf49049626417e2
+175, 0xbcd84af37d09ab91
+176, 0xbb41c177f3a3fa45
+177, 0x592becc814d55302
+178, 0xa88b4e65f6cfe5f7
+179, 0xa0a55e34ff879426
+180, 0x3c2ea6aa725b42b7
+181, 0x65ac4a407b1f9521
+182, 0xde63d53f7e88b556
+183, 0x18bc76696d015f40
+184, 0xd1363f2cd4c116a8
+185, 0x2fe859be19a48e4a
+186, 0x83d6099b1415e656
+187, 0x43f2cbc1a4ee6410
+188, 0xb2eca3d3421c533d
+189, 0xc52b98ea3f031f5d
+190, 0xfe57eb01da07e9d1
+191, 0xf9377883537a6031
+192, 0x364030c05dac7add
+193, 0x6815cb06b35d4404
+194, 0xceae2d4ce31894be
+195, 0xc602bcdf6062bf6a
+196, 0xc8e4bd8dcc6062e3
+197, 0x9c29e87b92a1a791
+198, 0x41e626b871ca9651
+199, 0x325c3d1fb8efbcd8
+200, 0x7dbbacf8e3419fb3
+201, 0x3602e72516bb7319
+202, 0x537a008ebd94d24b
+203, 0xda7714fc9d4d161d
+204, 0x1c8c73700e1b621b
+205, 0x2749b80937d6c939
+206, 0x76ee6abac5b14d33
+207, 0xf18d1e92cb6a8b5c
+208, 0x6ce9579d9291c721
+209, 0x60523c745a40e58
+210, 0x637f837fcc901757
+211, 0x2ff71b19661dc5b3
+212, 0x393ab586326ad16f
+213, 0xa0970ea30fe742b7
+214, 0x570222d7f27fe5ae
+215, 0x3b5806d43fd38629
+216, 0x129a0ad7420180c5
+217, 0x1c4726355778d52c
+218, 0x7c1459cf77656499
+219, 0xfe038a0932132069
+220, 0x4c4cc317a937483a
+221, 0xa333d24067e926ba
+222, 0x401d9b6ab37f6ef2
+223, 0x87ad0e491ebe4a2a
+224, 0xfc02f312e72d121d
+225, 0xfde715b3b99767b2
+226, 0xd111c342ba521c92
+227, 0x83b221b10879c617
+228, 0x6a1bf5c01fdf4277
+229, 0x166bfc0c3f5892ee
+230, 0x4608d556d7c57856
+231, 0x8d786857c95ece49
+232, 0x2d357445a1aca4ac
+233, 0x79620dae28ecd796
+234, 0x90e715dc0f2201c4
+235, 0x173b68b4c9f4b665
+236, 0x4e14d040ebac4eef
+237, 0xbd25960b4b892e
+238, 0x911a199db6f1989d
+239, 0xfe822d7c601fd2e0
+240, 0x9b4c1d58d8223a69
+241, 0x907c1891283843b0
+242, 0xf4868bf54061c4b2
+243, 0x17f8cd1fc24efd85
+244, 0xd44253f9af14c3aa
+245, 0x16d0da0cb911d43c
+246, 0x3c6a46615828e79a
+247, 0x498591c1138e11a5
+248, 0xcc0f26336d0d6141
+249, 0x4d3ebc873212309a
+250, 0x16bad7792d5c2c6a
+251, 0x474215a80b2bbd11
+252, 0x7159848abd8492fc
+253, 0x359341c50973685f
+254, 0x27512ee7bf784a4a
+255, 0x45228ea080f70447
+256, 0x880cab616500d50e
+257, 0x12fae93f9830d56e
+258, 0x6744ee64348d9acd
+259, 0x484dada28cd2a828
+260, 0x98491d0729e41863
+261, 0x2f15aac43c2863b0
+262, 0x5727a34d77a1da0f
+263, 0xa435cebef6a62eed
+264, 0xd211697d57b053b0
+265, 0x65aa757b68bd557
+266, 0xe3a1b7a2d8a3e06a
+267, 0x2adf64e67252a7a9
+268, 0xadadcb75cadee276
+269, 0x7934bc57ac8d97bf
+270, 0xccff0d0f412e0606
+271, 0x101a82aa3e8f3db9
+272, 0xb0f2498094b4575c
+273, 0xba2561d9ef26ed8a
+274, 0xfbcd1268fc3febe1
+275, 0x9aa10bb19eb152e0
+276, 0xf496217a601a6d72
+277, 0xe4be1e4f2fa91363
+278, 0x473a602bf3dd68eb
+279, 0xfe8ed2a48c26f4b5
+280, 0x20e94b1a00159476
+281, 0x93e1cb1c6af86ec7
+282, 0x4fcba3898f7442ba
+283, 0x5150c3a3d94891df
+284, 0x91cfce6c85b033ea
+285, 0x625e8a832a806491
+286, 0x28c97ba72e3ec0b2
+287, 0x8e172de217c71ea1
+288, 0x926b80216c732639
+289, 0x28b19431a649ae3d
+290, 0x57c039a6e95a3795
+291, 0xfbc354182fe52718
+292, 0x819dfd7c7d534cef
+293, 0xabb4093a619ed44f
+294, 0xe785b7ac6f656745
+295, 0xb647b4588b2f942f
+296, 0x64cf870a14c72d27
+297, 0x6d4a4a2a0ba9b37e
+298, 0x78bfb0427d7ce6b0
+299, 0x8dcc72b8bfc79ac6
+300, 0x1c14d915d5e76c99
+301, 0xaf48ddea6f096d79
+302, 0x51b39b67aa130d8
+303, 0x1aeeb39d4def06de
+304, 0xd678092ffedfdd27
+305, 0x8f54787f325111d3
+306, 0xf2ca2e827beaa6bc
+307, 0x339d134099e98545
+308, 0x1f6a8a7b33942e43
+309, 0x952c8065dbef669a
+310, 0xe066aeb6690147f7
+311, 0xed25aa92cf58ebb6
+312, 0x7601edce215ef521
+313, 0xed1c5b396abd9434
+314, 0x4fd1e407535de9d5
+315, 0xccc8315a0d4d1441
+316, 0x85753e250bb86976
+317, 0xf232e469378761c3
+318, 0x81d691b8e9aef3c6
+319, 0x224a2f9cab0ad0e
+320, 0x978f3d3e50007f4e
+321, 0xd3713e6a6c0cbe60
+322, 0xcce8f1eadd41f80d
+323, 0x34bda028a97d469
+324, 0x90e242fdf0f59183
+325, 0x4d749754fbc5f092
+326, 0x4399f5b7851cc87b
+327, 0xcb921a5f25f6c5d7
+328, 0x120bf5d0162101
+329, 0x1304cc2aa352735a
+330, 0xf7236c5d0d5d417b
+331, 0xc31b320fc1654306
+332, 0xb468c6b23f3fb4e7
+333, 0xb5985b5bfaca4166
+334, 0x898285a1cd2f8375
+335, 0xa13493da372aa7c9
+336, 0x15c80c09c12634e7
+337, 0x9b765c5cc9d438bd
+338, 0xee7da816a9201dcb
+339, 0x92e269f73b5a248e
+340, 0xa8086c5de81400ce
+341, 0xe0053901853d42be
+342, 0x821df32c012f433e
+343, 0x17a6d69ca37387c7
+344, 0x2b10044bfba3501f
+345, 0x8dfd262afc2e8515
+346, 0xd68c2c7b60226371
+347, 0xe81ac114e4416774
+348, 0x5896d60061ebc471
+349, 0xa996e3147811dbd1
+350, 0xa819c7b80ecb3661
+351, 0x982ad71b38afbc01
+352, 0xab152b65aa17b7fe
+353, 0x4582bc282ef187ef
+354, 0xab5a17fe8d9bc669
+355, 0x83664fa9cb0284b7
+356, 0x234c4b0091968f52
+357, 0x8ab5f51805688d37
+358, 0xe9e11186e0c53eda
+359, 0x10df37ef1de2eccf
+360, 0x780f1b0d52db968f
+361, 0x50bd4ff292872cd5
+362, 0x51e681c265f5ad0
+363, 0x842c49660a527566
+364, 0x6e56ee026e9eda87
+365, 0x4cf39e40d8c80393
+366, 0x13e466df371f7e1f
+367, 0xf2ce1799f38e028e
+368, 0x833c8db7adc6ff0e
+369, 0xc6e189abc2ec98f
+370, 0xafebb3721283fec5
+371, 0xb49bc1eb5cc17bdc
+372, 0xf1d02e818f5e4488
+373, 0xe5e9d5b41a1dd815
+374, 0xce8aca6573b1bfe5
+375, 0x9b0a5d70e268b1d5
+376, 0xf3c0503a8358f4de
+377, 0x2681605dd755669d
+378, 0xea265ca7601efc70
+379, 0xa93747f0a159439f
+380, 0x62a86ede78a23e50
+381, 0xac8a18935c3d063c
+382, 0x729c0a298f5059f5
+383, 0xbbf195e5b54399f4
+384, 0x38aa9d551f968900
+385, 0x3b3e700c58778caa
+386, 0x68e6e33c4443957a
+387, 0x7c56fc13eb269815
+388, 0xaf7daca39711804a
+389, 0x50fde6d10f9544b3
+390, 0xf3d37159f6f6c03d
+391, 0x82d298f5c1a71685
+392, 0x478661ac54c5002c
+393, 0x6053768e1a324ae0
+394, 0xde8fb4a7e56707ea
+395, 0xaa2809301faa8cf4
+396, 0x690a8d49fedd0722
+397, 0xe17c481b9c217de9
+398, 0x60d1d8a2b57288e3
+399, 0x149adfaadc6b0886
+400, 0xa3c18b6eb79cd5fa
+401, 0x5774e3a091af5f58
+402, 0x2acca57ff30e5712
+403, 0x94454d67367c4b0c
+404, 0x581b2985ac2df5ca
+405, 0x71618e50744f3e70
+406, 0x270a7f3bd9a94ae6
+407, 0x3ef81af9bb36cd7b
+408, 0x8a4a2592875254aa
+409, 0x704ac6086fbb414a
+410, 0xda774d5d3f57414d
+411, 0xe20d3358b918ae9e
+412, 0x934a6b9f7b91e247
+413, 0xf91649cde87ec42c
+414, 0x248cec5f9b6ced30
+415, 0x56791809fd8d64ba
+416, 0xf502b2765c1395f
+417, 0x6b04ec973d75aa7f
+418, 0xb0339f2794bb26f
+419, 0x4c524636efbaea49
+420, 0x6bbf3876e9738748
+421, 0xf686524e754e9e24
+422, 0x8dafa05a42d19cd3
+423, 0xc5f069ab2434008e
+424, 0x4fd64cc713cba76
+425, 0xdbf93450c881ed5f
+426, 0x492e278ebabb59a2
+427, 0x993fddfde4542642
+428, 0xecde68a72c8d4e52
+429, 0xe0760b3074c311fd
+430, 0x68dc0e7e06528707
+431, 0x52b50edf49c0fdc7
+432, 0xb2bd4185c138f412
+433, 0x431496d7e1d86f3
+434, 0xa4e605b037e26c44
+435, 0x58236ae1f0aca2b5
+436, 0x26c72c420fc314d8
+437, 0x20134e982ab99a2b
+438, 0x544b59b8b211374b
+439, 0x1301c42f3a14d993
+440, 0x52a6ea740f763b0f
+441, 0xf209d70c2bebf119
+442, 0xac66a4ebc2aa1be
+443, 0x683713ed35878788
+444, 0x2b5578acec06b80c
+445, 0x86428efa11c45b36
+446, 0xb49010adb17d291e
+447, 0x73b686bd8664b6be
+448, 0x6d28ebf57b6884cc
+449, 0x9712091230ff58d9
+450, 0xc9c91f74c38b286
+451, 0x776310ac41dc008e
+452, 0x2f3739df0bf6a88e
+453, 0x5792dc62b94db675
+454, 0x5715910d024b06af
+455, 0xeb1dd745458da08
+456, 0xfce7b07ccfa851a7
+457, 0xc305f1e983ac368
+458, 0x485aa9519ac00bb0
+459, 0xa5354f6589fb0ea0
+460, 0x32fee02dfdbf4454
+461, 0x4d1ddc304bbefaaa
+462, 0x789a270a1737e57e
+463, 0x9f3072f4b1ed8156
+464, 0x4de3c00e89058120
+465, 0xb00a02529e0a86fa
+466, 0x539f6f0edd845d9a
+467, 0x85e578fe15a8c001
+468, 0xa12c8e1a72cce7d8
+469, 0xc6908abbc2b1828
+470, 0xcf70090774cbb38c
+471, 0x3b636a6977b45d4a
+472, 0xf0a731b220680b57
+473, 0x18973929f51443a8
+474, 0xe93e1fbe7eadabe
+475, 0x8233730f0a6dfa02
+476, 0x66e50b6919b0ab74
+477, 0xb1aba87c97fd08a2
+478, 0xd4dffc1fbc117ad6
+479, 0x6f7fa65724b96e6a
+480, 0x4bd5800dee92e0fa
+481, 0xe18a959db6256da
+482, 0xe53a291bc66df487
+483, 0xb7ec306a08651806
+484, 0x1847a6b80d2821e1
+485, 0xda50391283b14d39
+486, 0xacc4d3cd7cceb97a
+487, 0x57f70185165b7bc6
+488, 0x302b6d597c3aaba7
+489, 0xa47f32d037eab51e
+490, 0xe1509b4408abc559
+491, 0x4f30a1d7c2934157
+492, 0x2ad03e6c60b650b2
+493, 0x334d9c337b0a9064
+494, 0xc7f442821e7aac12
+495, 0xbcdeb09298694cdd
+496, 0xe42402389f8f0fb4
+497, 0xe5de56af539df727
+498, 0x7017f9b2101ee240
+499, 0x1ee5e68d5b10001d
+500, 0x436229051836387a
+501, 0xcd532d6d6ec38fb7
+502, 0x30a66606fdf38272
+503, 0xfdaa2ab9cf798496
+504, 0x4277b4adec70e7df
+505, 0x72cfc30256e0eaef
+506, 0x3c3359fd9bd34917
+507, 0xb7aa89598856efb0
+508, 0xf72226f8bf299ef5
+509, 0x258c499275a4356f
+510, 0x999a56bfc7f20d76
+511, 0x2b3e7432e20c18b
+512, 0x2d1251332f760cb5
+513, 0x7420e0eea62157c5
+514, 0xe85c895aa27cec3d
+515, 0x27a0545c7020d57c
+516, 0xc68638a65b4fff0d
+517, 0xfda473983a4ea747
+518, 0xd19fe65fb4c06062
+519, 0x6b1374e050ee15e4
+520, 0x80065ecd49bc4bef
+521, 0x4ee655954bc838de
+522, 0xe8fb777504a72299
+523, 0x86b652ea70f4bdde
+524, 0xcdc9e0fbde7e4f33
+525, 0x352c0a50cd3ac56
+526, 0x4b8605d368be75dc
+527, 0x1ac9ea8129efbc37
+528, 0x470325faa99f39c5
+529, 0x25dd7ef9adccf7a1
+530, 0x5ae2c7a03e965816
+531, 0xf733d2df59dacc7d
+532, 0xa05bbf0a8a1a7a70
+533, 0xe8aa3f102846ef5f
+534, 0xc9b85ec49ae71789
+535, 0xb904c14ed1cb1936
+536, 0x5ae618230b5f0444
+537, 0x97987fe47b5d7467
+538, 0xabb3aca8865ca761
+539, 0x38bfdf29d4508228
+540, 0x353654f408353330
+541, 0xeb7e92930ae4ef0d
+542, 0xec50f1a7ca526b96
+543, 0xd5e2dc08b5697544
+544, 0x24c7fd69d5ec32df
+545, 0x6f7e1095568b8620
+546, 0x6ed9c16ca13b3c8
+547, 0xe676ef460002130f
+548, 0xa3a01a3992c4b430
+549, 0xe2130406c3b1f202
+550, 0xa8f7263e2aedcd20
+551, 0xc45d71ef2e35f507
+552, 0x37155594021da7ba
+553, 0x22dc94f19de73159
+554, 0x7969fc6bffc5443f
+555, 0x97def7e44faa6bfe
+556, 0x8b940f5e8931d71f
+557, 0xd95b1dd3f1a3fdd5
+558, 0x1c83bfdca615701a
+559, 0xb7fcb56279ceca6b
+560, 0xd84f8950f20dcd0
+561, 0xb03343698de3cbe0
+562, 0xf64565d448d71f71
+563, 0xda52b4676e0ae662
+564, 0xda39c2c05b4ffb91
+565, 0xb35e2560421f6a85
+566, 0x1a7b108d48ac3646
+567, 0xc4e264dc390d79ed
+568, 0xa10727dfd9813256
+569, 0x40d23154e720e4f7
+570, 0xd9fa7cd7e313e119
+571, 0xcbf29107859e6013
+572, 0xc357338553d940b7
+573, 0x2641b7ab0bdfcbaa
+574, 0xd12f2b6060533ae7
+575, 0xd0435aa626411c56
+576, 0x44af4a488a9cec72
+577, 0xb934232ea8fa5696
+578, 0x760a8b12072b572d
+579, 0xfab18f9942cfa9b3
+580, 0x5676834c1fe84d16
+581, 0x9c54e4fddb353236
+582, 0xab49edfc9551f293
+583, 0x567f1fb45a871d
+584, 0x32a967c873998834
+585, 0x99240aad380ef8d1
+586, 0x7f66cbd432859a64
+587, 0x4cdc8a4658166822
+588, 0x984e3984a5766492
+589, 0xa3b2d0a3d64d3d94
+590, 0x177f667172f2affc
+591, 0xb1a90607a73a303f
+592, 0xe600b6c36427f878
+593, 0xf758f9834cb7f466
+594, 0x8ee9fce4a3f36449
+595, 0xcb8f11533e7da347
+596, 0xe7cf647794dabd7c
+597, 0xc9d92cfe6110806
+598, 0xea1335fa9145a1ec
+599, 0xbc6c29821d094552
+600, 0x37b9d6a858cc8bc3
+601, 0xf24e4c694929893e
+602, 0x55d025ce2d7d0004
+603, 0xccdc69acccf4267b
+604, 0xc491c04340c222eb
+605, 0xba50f75ecec9befb
+606, 0x1ec7bd85b8fe3bb9
+607, 0xe4de66498c59ae8a
+608, 0x38aa9e912712c889
+609, 0xcee0e43c5cc31566
+610, 0x72b69aa708fc7ed
+611, 0xdff70b7f6fa96679
+612, 0xd6d71d82112aadc3
+613, 0x365177892cb78531
+614, 0xa54852b39de4f72c
+615, 0x11dd5832bf16dd59
+616, 0x248a0f3369c97097
+617, 0xa14cec0260e26792
+618, 0x3517616ff142bed1
+619, 0x9b693ad39dab7636
+620, 0x739dff825e994434
+621, 0x67711e7356098c9
+622, 0xa81f8515d2fdf458
+623, 0xdac2908113fe568e
+624, 0xe99944ebc6e2806a
+625, 0x671728ca5b030975
+626, 0xfdad20edb2b4a789
+627, 0xedc6e466bd0369d2
+628, 0x88b5d469821f7e1b
+629, 0x2eabf94049a522a5
+630, 0x247794b7a2f5a8e3
+631, 0x278942bdbe02c649
+632, 0xbe5a9a9196ab99c1
+633, 0x75955060866da1b5
+634, 0xdedcfa149273c0b5
+635, 0xdbeb7a57758f3867
+636, 0x7b9053347a2c8d5a
+637, 0xa059b3f2eed338a5
+638, 0x59401a46ded3b79f
+639, 0x38044ba56a6d19fb
+640, 0x72c7221b4e77e779
+641, 0x526df3491a3a34da
+642, 0xc3b31184ba16c0c2
+643, 0xd94c7144488624af
+644, 0xcf966ee4dc373f91
+645, 0x62049e65dd416266
+646, 0x7c2adccb925bf8f
+647, 0xd5fa5c22ed4ef8e1
+648, 0xd00134ebd11f2cd1
+649, 0xfbdf81767bed3634
+650, 0x62e8cc8ff66b6e26
+651, 0x3a72d6bcd4f2dcf7
+652, 0xf1cd45b1b46a86ed
+653, 0x1271f98e0938bb9a
+654, 0x82e6927e83dc31fa
+655, 0x7b9b0e0acb67b92d
+656, 0x6df503e397b2e701
+657, 0x93888f6fb561e0c3
+658, 0x393fb6069a40291
+659, 0x967a7d894cc0754d
+660, 0x6e298996ad866333
+661, 0x5ff3cf5559d6ab46
+662, 0xd0d70508c40349f5
+663, 0xc64c66c0dd426b33
+664, 0x8fea340ee35c64dd
+665, 0xf9cd381eb3060005
+666, 0xfcc37c2799fc0b11
+667, 0x6a37c91d65b489fa
+668, 0x57231000fa0a0c9d
+669, 0x55f6e292c6703f9a
+670, 0xd0508ffbfa55a7a6
+671, 0x885db543276bdac8
+672, 0xc26dbe6a26b0e704
+673, 0x21f884874ebd709e
+674, 0x711f0b6c8f732220
+675, 0x354d0a361eaee195
+676, 0x721344d8d30b006a
+677, 0xa0e090a0d3a56f07
+678, 0x16b3d5d823a4952b
+679, 0x59d7874bc9eae7b6
+680, 0x9bbb32710076455f
+681, 0xd4fb22242ffabafd
+682, 0xe1d4ac6770be1d89
+683, 0xb259cedebc73dc8a
+684, 0x35faaa3b4246ab69
+685, 0x5d26addefdaee89
+686, 0x8e7ec350da0f3545
+687, 0xd0f316eed9f8fc79
+688, 0x98b2a52c9bf291b2
+689, 0xe4d294a8aca6a314
+690, 0x25bd554e6aa7673c
+691, 0xcfde5dcba5be2a6c
+692, 0xb5e01fb48d2d2107
+693, 0xe1caf28948028536
+694, 0xd434aa0a26f3ee9b
+695, 0xd17723381641b8f6
+696, 0xfe73bd1f3f3768a2
+697, 0x1cc6b1abd08d67e9
+698, 0x247e328371a28de0
+699, 0x502e7942e5a9104a
+700, 0x6a030fd242eb4502
+701, 0xa2ffe02744014ce8
+702, 0x59290763b18fe04e
+703, 0xcf14241564271436
+704, 0xb0fb73c3c1503aff
+705, 0x94e27c622f82137a
+706, 0x747a5b406ac3e1f0
+707, 0x9a914e96a732031d
+708, 0x59f68c6c8f078835
+709, 0x809d012c73eb4724
+710, 0x5b3c3b73e1b37d74
+711, 0xdde60ef3ba49cdf7
+712, 0x87a14e1f9c761986
+713, 0x4109b960604522af
+714, 0x122d0e1ed0eb6bb9
+715, 0xadc0d29e80bfe33
+716, 0xa25b1b44f5fc8e4e
+717, 0xbab85d8a9b793f20
+718, 0x825f4cbced0e7d1e
+719, 0x2d6ae8807acb37ea
+720, 0x8234420adce2e39
+721, 0x4a8ad4da6b804807
+722, 0x1e19f9bc215e5245
+723, 0x1d6f4848a916dd5e
+724, 0x9ac40dfcdc2d39cc
+725, 0x9f3524e3086155ec
+726, 0x861fffc43124b2ef
+727, 0xe640e3b756396372
+728, 0x41cb0f0c5e149669
+729, 0xe0bd37e1192e4205
+730, 0x62917d3858f4ce47
+731, 0xa36e7eb4d855820a
+732, 0x204b90255a3bf724
+733, 0x66ee83a0175535bc
+734, 0x2c14ce7c6b0c1423
+735, 0x85d9495fa514f70d
+736, 0x5a4fe45ead874dbc
+737, 0xe72248dcb8cfc863
+738, 0xfc21ff2932ed98cd
+739, 0xcbba1edd735b5cad
+740, 0x91ddc32809679bf5
+741, 0x192cdf2c7631ea1f
+742, 0xbbc451ddf2ea286f
+743, 0xad9e80cae2397a64
+744, 0x6918f0119b95d0e5
+745, 0xa40379017a27d70a
+746, 0x1aaeddb600e61e1
+747, 0x15afd93cbd7adda9
+748, 0x156719bc2b757ff4
+749, 0x13d9a59e2b2df49d
+750, 0x9a490986eaddf0a
+751, 0xef9a350f0b3eb6b4
+752, 0x5de7f6295ba4fa4d
+753, 0x7f37fd087c3fdb49
+754, 0xa9fe3749d6f3f209
+755, 0x50912ac036d9bfb
+756, 0x982cb4d726a441f8
+757, 0x8ca8d8af59b872d0
+758, 0x7f8adfb0ceeade8a
+759, 0xdad390ec742be44
+760, 0xa637944d0045be5b
+761, 0x3569a3b3af807061
+762, 0x9599da8eae14511d
+763, 0xc333e8d19589b01a
+764, 0xfb9b524a20b571e1
+765, 0xbd9dc8b37ce5c3e1
+766, 0x142333005fa389ac
+767, 0x1368bc37cd5bcce1
+768, 0x16094907ad6ecf73
+769, 0xb32c90dbba4c1130
+770, 0x82761d97c1747dd0
+771, 0x599f9f267ae3444d
+772, 0x79ad3382994852e1
+773, 0x2511f06d9ef06e54
+774, 0xb35e6ab7d5bbddae
+775, 0xfca9fa83a2988732
+776, 0x7d4350f0394ac3ba
+777, 0xa52a9527bb176ea3
+778, 0xb49fa0ceb2aa8353
+779, 0x1f62e504d1468cc0
+780, 0xe1a77bfccce6efc3
+781, 0x776cdff4dc0d6797
+782, 0x56612e39b652c1f2
+783, 0x5f096a29294eda04
+784, 0x7978abc3aabd8b23
+785, 0x79dd875e0485b979
+786, 0x8a98aa4d5735d778
+787, 0xcca43940f69d2388
+788, 0xb2d4b156f144f93a
+789, 0xbd528a676e9a862
+790, 0x2a394939c8e7ec5e
+791, 0xb1da900c6efe4abc
+792, 0x9869af479de4c034
+793, 0x78dbdfb88ac7c1db
+794, 0x18cb169143088041
+795, 0xe69e5461c51a3e13
+796, 0x5389fa16ea98183c
+797, 0xed7c80d1be1ea520
+798, 0x87246fc359758ced
+799, 0xab323eba95fae4ed
+800, 0xbc4c0dde7f8a1828
+801, 0xdb739f7955610b1a
+802, 0xecd8c68c3434cc
+803, 0x138c2eb88c477f44
+804, 0x28a65f96727aae41
+805, 0xdee879f2cf5629d
+806, 0x684f0c90ef20070f
+807, 0xa24a819ef5621800
+808, 0x8d0054f870e4fdcb
+809, 0x99e8c6e695b600b
+810, 0x50b705245891f7c3
+811, 0xc02eed3a6e58e51a
+812, 0x443d64e95443606c
+813, 0xca24959cfbd2d120
+814, 0xe072609ea48815bc
+815, 0xbcc715026590315b
+816, 0x3e76df24d7aa5938
+817, 0xd8ff04940d9b79ae
+818, 0x54474ce790059bcd
+819, 0x278390dd6aa70e81
+820, 0xf4df619fe35414e4
+821, 0x757d71270264e615
+822, 0x1e8a373699c11b23
+823, 0xef68c82046e67dd6
+824, 0xe280006599972620
+825, 0x234e095183b0f4d6
+826, 0xe3b7560ed9839749
+827, 0xcd5ec4086572332e
+828, 0xc41c0d4aaa279108
+829, 0x4b9cd6126bc16a6d
+830, 0x4a7252734f3e3dd0
+831, 0xb3132df156cc103a
+832, 0xf9e4abbf7b64464a
+833, 0xf936df27fb3c47b7
+834, 0x9142960873f6d71a
+835, 0x4ba6aa3235cdb10d
+836, 0x3237a2e765ba7766
+837, 0xd62f0b94c8e99e54
+838, 0x26b682f90a3ae41b
+839, 0x40ad5e82072b6f81
+840, 0xd0198101f5484000
+841, 0xe4fac60ba11c332
+842, 0x472d0b0a95ef9d38
+843, 0x8512557aec5a3d8f
+844, 0xef83169d3efd4de9
+845, 0x53fe89283e7a7676
+846, 0x2f50933053d69fc4
+847, 0x76f5e4362e2e53a2
+848, 0x8676fdccce28874a
+849, 0x2737764c1fb1f821
+850, 0x4a6f70afc066ab55
+851, 0x27f8e151e310fca4
+852, 0xd606960ccbe85161
+853, 0xcce51d7ddd270a32
+854, 0xb4235999794875c2
+855, 0x580084e358e884
+856, 0x2159d5e6dc8586d7
+857, 0x87bd54d8599b3ba4
+858, 0x3e9ade6a2181664
+859, 0x5e6e140406d97623
+860, 0x511545d5aa0080a2
+861, 0xf49d78ed219aac57
+862, 0xbece1f9c90b8ea87
+863, 0x1c741cac36a2c514
+864, 0x7453c141047db967
+865, 0xd751832a5037eba2
+866, 0x71370a3f30ada1f7
+867, 0x7c01cf2dcb408631
+868, 0x1052a4fbdccc0fa1
+869, 0x13d525c9df3fb6c
+870, 0xa3aa8dbfee760c55
+871, 0xc0288d200f5155cf
+872, 0x79f4bcd12af567c3
+873, 0x8160d163bb548755
+874, 0x5cf2995fb69fd2df
+875, 0xcc98ed01396639df
+876, 0xad95f1d9cfc8256e
+877, 0xa3df27d9fbdbfb9d
+878, 0x83e5f5dda4d52929
+879, 0x9adc05043009f55b
+880, 0xdfe8329dfde1c001
+881, 0x9980ccdd5298e6a2
+882, 0x636a7bd134f6ef56
+883, 0xef5ff780c4be6ba4
+884, 0x290d71dc77a56d16
+885, 0x6d65db9ff58de1e6
+886, 0x944b063b3805a696
+887, 0xce468ca2cce33008
+888, 0x5ba1ccb840f80f48
+889, 0x28ddce36fc9ad268
+890, 0x4f77ef254d507a21
+891, 0xce9b4057fadf3ab
+892, 0xb518bc68298730e6
+893, 0xd2eb5b8e2ec665b0
+894, 0xe1583303a4f87344
+895, 0x9d5a0df4fbe1bed5
+896, 0x2ba9bc03ec8cfd07
+897, 0x479ed880a96ca669
+898, 0xcedf96338324771a
+899, 0x312f4fc2da41ffaa
+900, 0xa0eb9cf23b5e1ed8
+901, 0xf8f88f975dc3f539
+902, 0x4a37e185d0e96e0f
+903, 0xf829654a5c0b46f9
+904, 0x3909cca7a7f8c7fb
+905, 0x4c2e1d66ceb45105
+906, 0xaffaa19e1db8af87
+907, 0x9ec498246bd18c76
+908, 0x21d51558edc089da
+909, 0xe8984112cd1b1561
+910, 0x7de1d2cf54b0c0e1
+911, 0xa06729aed50bfb9d
+912, 0xcf19f733e5db19e1
+913, 0x70edf2624ab777cd
+914, 0x46685becad10e078
+915, 0x825e0f6add46785
+916, 0x66d4af3b15f70de4
+917, 0xc676614b0666b21
+918, 0x282a916c864f5cb7
+919, 0x2707283a3f512167
+920, 0x37ff3afda7461623
+921, 0xc767eb1205e4ca86
+922, 0x46b359aecc4ea25b
+923, 0x67fbbb797a16dbb1
+924, 0x64fd4ba57122290e
+925, 0x8acc2a8ae59d8fac
+926, 0x64a49298599acc67
+927, 0xedf00de67177ce30
+928, 0x1ea9d8d7e76d2d2c
+929, 0x363fcac323f70eb2
+930, 0x19e6e3ec8a9712eb
+931, 0xca541e96b0961f09
+932, 0x4d8fd34c2822ec46
+933, 0x2fdd56a50b32f705
+934, 0xaac2fcf251e3fd3
+935, 0xb0c600299e57045c
+936, 0xd951ec589e909e38
+937, 0x4dc8414390cae508
+938, 0x537ef9d5e2321344
+939, 0xa57bc21fd31aa2dc
+940, 0xa3a60df564183750
+941, 0xbe69a5ce2e369fb6
+942, 0x7744601f4c053ec8
+943, 0x3838452af42f2612
+944, 0xd4f0dad7115a54e9
+945, 0x629cf68d8009a624
+946, 0x2211c8fa34cb98cb
+947, 0x8040b19e2213db83
+948, 0xb2a86d3ba2384fd
+949, 0x4b85cec4f93f0dab
+950, 0xc8d212d21ea6845d
+951, 0x5b271a03a4fe2be0
+952, 0xff4f671319ad8434
+953, 0x8e615a919d5afa96
+954, 0xea7f47c53161160a
+955, 0x33273930b13c6efc
+956, 0x98eedda27fb59c3c
+957, 0x188dc5e92e939677
+958, 0x9dbd0fa0911430f1
+959, 0x5b3dcf3fa75dfd2b
+960, 0x3f03846febdb275d
+961, 0x20cc24faea9e9cf6
+962, 0x854f3ac66199ff5d
+963, 0x31169ac99d341e6f
+964, 0xa85daed3c0bc1bbe
+965, 0x64633711e71ba5dd
+966, 0x530e79978dc73334
+967, 0x636f2ee6e20aef13
+968, 0xf6220f8b6d9a58fb
+969, 0x425db8fa32141a7b
+970, 0xac7c210f4b02be95
+971, 0x5fe8cfbe197a7754
+972, 0xfff7d40c79420ea
+973, 0x5f8bab9ef4697b77
+974, 0xaf6fe54e45b23fe8
+975, 0xce79456ccc70bbce
+976, 0x645ef680f48f1c00
+977, 0xa4dfac46e2028595
+978, 0x6bece4c41effc5df
+979, 0xd316df886442641f
+980, 0xa4f6ff994edd2a6
+981, 0x30281ae3cc49abe4
+982, 0x39acb7b663dea974
+983, 0x5e8829b01a7c06fb
+984, 0x87bdb08cf027f13e
+985, 0xdfa5ede784e802f6
+986, 0x46d03d55711c38cc
+987, 0xa55a961fc9788306
+988, 0xbf09ded495a2e57a
+989, 0xcd601b29a639cc16
+990, 0x2193ce026bfd1085
+991, 0x25ba27f3f225be13
+992, 0x6f685be82f64f2fe
+993, 0xec8454108229c450
+994, 0x6e79d8d205447a44
+995, 0x9ed7b6a96b9ccd68
+996, 0xae7134b3b7f8ee37
+997, 0x66963de0e5ebcc02
+998, 0x29c8dcd0d17c423f
+999, 0xfb8482c827eb90bc

parrot/lib/python3.10/site-packages/scipy/_lib/__init__.py

@@ -0,0 +1,14 @@
+"""
+Module containing private utility functions
+===========================================
+
+The ``scipy._lib`` namespace is empty (for now). Tests for all
+utilities in submodules of ``_lib`` can be run with::
+
+    from scipy import _lib
+    _lib.test()
+
+"""
+from scipy._lib._testutils import PytestTester
+test = PytestTester(__name__)
+del PytestTester

parrot/lib/python3.10/site-packages/scipy/_lib/_docscrape.py

@@ -0,0 +1,679 @@
+"""Extract reference documentation from the NumPy source tree.
+
+"""
+# copied from numpydoc/docscrape.py
+import inspect
+import textwrap
+import re
+import pydoc
+from warnings import warn
+from collections import namedtuple
+from collections.abc import Callable, Mapping
+import copy
+import sys
+
+
+def strip_blank_lines(l):
+    "Remove leading and trailing blank lines from a list of lines"
+    while l and not l[0].strip():
+        del l[0]
+    while l and not l[-1].strip():
+        del l[-1]
+    return l
+
+
+class Reader:
+    """A line-based string reader.
+
+    """
+    def __init__(self, data):
+        """
+        Parameters
+        ----------
+        data : str
+           String with lines separated by '\\n'.
+
+        """
+        if isinstance(data, list):
+            self._str = data
+        else:
+            self._str = data.split('\n')  # store string as list of lines
+
+        self.reset()
+
+    def __getitem__(self, n):
+        return self._str[n]
+
+    def reset(self):
+        self._l = 0  # current line nr
+
+    def read(self):
+        if not self.eof():
+            out = self[self._l]
+            self._l += 1
+            return out
+        else:
+            return ''
+
+    def seek_next_non_empty_line(self):
+        for l in self[self._l:]:
+            if l.strip():
+                break
+            else:
+                self._l += 1
+
+    def eof(self):
+        return self._l >= len(self._str)
+
+    def read_to_condition(self, condition_func):
+        start = self._l
+        for line in self[start:]:
+            if condition_func(line):
+                return self[start:self._l]
+            self._l += 1
+            if self.eof():
+                return self[start:self._l+1]
+        return []
+
+    def read_to_next_empty_line(self):
+        self.seek_next_non_empty_line()
+
+        def is_empty(line):
+            return not line.strip()
+
+        return self.read_to_condition(is_empty)
+
+    def read_to_next_unindented_line(self):
+        def is_unindented(line):
+            return (line.strip() and (len(line.lstrip()) == len(line)))
+        return self.read_to_condition(is_unindented)
+
+    def peek(self, n=0):
+        if self._l + n < len(self._str):
+            return self[self._l + n]
+        else:
+            return ''
+
+    def is_empty(self):
+        return not ''.join(self._str).strip()
+
+
+class ParseError(Exception):
+    def __str__(self):
+        message = self.args[0]
+        if hasattr(self, 'docstring'):
+            message = f"{message} in {self.docstring!r}"
+        return message
+
+
+Parameter = namedtuple('Parameter', ['name', 'type', 'desc'])
+
+
+class NumpyDocString(Mapping):
+    """Parses a numpydoc string to an abstract representation
+
+    Instances define a mapping from section title to structured data.
+
+    """
+
+    sections = {
+        'Signature': '',
+        'Summary': [''],
+        'Extended Summary': [],
+        'Parameters': [],
+        'Returns': [],
+        'Yields': [],
+        'Receives': [],
+        'Raises': [],
+        'Warns': [],
+        'Other Parameters': [],
+        'Attributes': [],
+        'Methods': [],
+        'See Also': [],
+        'Notes': [],
+        'Warnings': [],
+        'References': '',
+        'Examples': '',
+        'index': {}
+    }
+
+    def __init__(self, docstring, config={}):
+        orig_docstring = docstring
+        docstring = textwrap.dedent(docstring).split('\n')
+
+        self._doc = Reader(docstring)
+        self._parsed_data = copy.deepcopy(self.sections)
+
+        try:
+            self._parse()
+        except ParseError as e:
+            e.docstring = orig_docstring
+            raise
+
+    def __getitem__(self, key):
+        return self._parsed_data[key]
+
+    def __setitem__(self, key, val):
+        if key not in self._parsed_data:
+            self._error_location("Unknown section %s" % key, error=False)
+        else:
+            self._parsed_data[key] = val
+
+    def __iter__(self):
+        return iter(self._parsed_data)
+
+    def __len__(self):
+        return len(self._parsed_data)
+
+    def _is_at_section(self):
+        self._doc.seek_next_non_empty_line()
+
+        if self._doc.eof():
+            return False
+
+        l1 = self._doc.peek().strip()  # e.g. Parameters
+
+        if l1.startswith('.. index::'):
+            return True
+
+        l2 = self._doc.peek(1).strip()  # ---------- or ==========
+        return l2.startswith('-'*len(l1)) or l2.startswith('='*len(l1))
+
+    def _strip(self, doc):
+        i = 0
+        j = 0
+        for i, line in enumerate(doc):
+            if line.strip():
+                break
+
+        for j, line in enumerate(doc[::-1]):
+            if line.strip():
+                break
+
+        return doc[i:len(doc)-j]
+
+    def _read_to_next_section(self):
+        section = self._doc.read_to_next_empty_line()
+
+        while not self._is_at_section() and not self._doc.eof():
+            if not self._doc.peek(-1).strip():  # previous line was empty
+                section += ['']
+
+            section += self._doc.read_to_next_empty_line()
+
+        return section
+
+    def _read_sections(self):
+        while not self._doc.eof():
+            data = self._read_to_next_section()
+            name = data[0].strip()
+
+            if name.startswith('..'):  # index section
+                yield name, data[1:]
+            elif len(data) < 2:
+                yield StopIteration
+            else:
+                yield name, self._strip(data[2:])
+
+    def _parse_param_list(self, content, single_element_is_type=False):
+        r = Reader(content)
+        params = []
+        while not r.eof():
+            header = r.read().strip()
+            if ' : ' in header:
+                arg_name, arg_type = header.split(' : ')[:2]
+            else:
+                if single_element_is_type:
+                    arg_name, arg_type = '', header
+                else:
+                    arg_name, arg_type = header, ''
+
+            desc = r.read_to_next_unindented_line()
+            desc = dedent_lines(desc)
+            desc = strip_blank_lines(desc)
+
+            params.append(Parameter(arg_name, arg_type, desc))
+
+        return params
+
+    # See also supports the following formats.
+    #
+    # <FUNCNAME>
+    # <FUNCNAME> SPACE* COLON SPACE+ <DESC> SPACE*
+    # <FUNCNAME> ( COMMA SPACE+ <FUNCNAME>)+ (COMMA | PERIOD)? SPACE*
+    # <FUNCNAME> ( COMMA SPACE+ <FUNCNAME>)* SPACE* COLON SPACE+ <DESC> SPACE*
+
+    # <FUNCNAME> is one of
+    #   <PLAIN_FUNCNAME>
+    #   COLON <ROLE> COLON BACKTICK <PLAIN_FUNCNAME> BACKTICK
+    # where
+    #   <PLAIN_FUNCNAME> is a legal function name, and
+    #   <ROLE> is any nonempty sequence of word characters.
+    # Examples: func_f1  :meth:`func_h1` :obj:`~baz.obj_r` :class:`class_j`
+    # <DESC> is a string describing the function.
+
+    _role = r":(?P<role>\w+):"
+    _funcbacktick = r"`(?P<name>(?:~\w+\.)?[a-zA-Z0-9_\.-]+)`"
+    _funcplain = r"(?P<name2>[a-zA-Z0-9_\.-]+)"
+    _funcname = r"(" + _role + _funcbacktick + r"|" + _funcplain + r")"
+    _funcnamenext = _funcname.replace('role', 'rolenext')
+    _funcnamenext = _funcnamenext.replace('name', 'namenext')
+    _description = r"(?P<description>\s*:(\s+(?P<desc>\S+.*))?)?\s*$"
+    _func_rgx = re.compile(r"^\s*" + _funcname + r"\s*")
+    _line_rgx = re.compile(
+        r"^\s*" +
+        r"(?P<allfuncs>" +        # group for all function names
+        _funcname +
+        r"(?P<morefuncs>([,]\s+" + _funcnamenext + r")*)" +
+        r")" +                     # end of "allfuncs"
+        # Some function lists have a trailing comma (or period)  '\s*'
+        r"(?P<trailing>[,\.])?" +
+        _description)
+
+    # Empty <DESC> elements are replaced with '..'
+    empty_description = '..'
+
+    def _parse_see_also(self, content):
+        """
+        func_name : Descriptive text
+            continued text
+        another_func_name : Descriptive text
+        func_name1, func_name2, :meth:`func_name`, func_name3
+
+        """
+
+        items = []
+
+        def parse_item_name(text):
+            """Match ':role:`name`' or 'name'."""
+            m = self._func_rgx.match(text)
+            if not m:
+                raise ParseError("%s is not a item name" % text)
+            role = m.group('role')
+            name = m.group('name') if role else m.group('name2')
+            return name, role, m.end()
+
+        rest = []
+        for line in content:
+            if not line.strip():
+                continue
+
+            line_match = self._line_rgx.match(line)
+            description = None
+            if line_match:
+                description = line_match.group('desc')
+                if line_match.group('trailing') and description:
+                    self._error_location(
+                        'Unexpected comma or period after function list at '
+                        'index %d of line "%s"' % (line_match.end('trailing'),
+                                                   line),
+                        error=False)
+            if not description and line.startswith(' '):
+                rest.append(line.strip())
+            elif line_match:
+                funcs = []
+                text = line_match.group('allfuncs')
+                while True:
+                    if not text.strip():
+                        break
+                    name, role, match_end = parse_item_name(text)
+                    funcs.append((name, role))
+                    text = text[match_end:].strip()
+                    if text and text[0] == ',':
+                        text = text[1:].strip()
+                rest = list(filter(None, [description]))
+                items.append((funcs, rest))
+            else:
+                raise ParseError("%s is not a item name" % line)
+        return items
+
+    def _parse_index(self, section, content):
+        """
+        .. index:: default
+           :refguide: something, else, and more
+
+        """
+        def strip_each_in(lst):
+            return [s.strip() for s in lst]
+
+        out = {}
+        section = section.split('::')
+        if len(section) > 1:
+            out['default'] = strip_each_in(section[1].split(','))[0]
+        for line in content:
+            line = line.split(':')
+            if len(line) > 2:
+                out[line[1]] = strip_each_in(line[2].split(','))
+        return out
+
+    def _parse_summary(self):
+        """Grab signature (if given) and summary"""
+        if self._is_at_section():
+            return
+
+        # If several signatures present, take the last one
+        while True:
+            summary = self._doc.read_to_next_empty_line()
+            summary_str = " ".join([s.strip() for s in summary]).strip()
+            compiled = re.compile(r'^([\w., ]+=)?\s*[\w\.]+\(.*\)$')
+            if compiled.match(summary_str):
+                self['Signature'] = summary_str
+                if not self._is_at_section():
+                    continue
+            break
+
+        if summary is not None:
+            self['Summary'] = summary
+
+        if not self._is_at_section():
+            self['Extended Summary'] = self._read_to_next_section()
+
+    def _parse(self):
+        self._doc.reset()
+        self._parse_summary()
+
+        sections = list(self._read_sections())
+        section_names = {section for section, content in sections}
+
+        has_returns = 'Returns' in section_names
+        has_yields = 'Yields' in section_names
+        # We could do more tests, but we are not. Arbitrarily.
+        if has_returns and has_yields:
+            msg = 'Docstring contains both a Returns and Yields section.'
+            raise ValueError(msg)
+        if not has_yields and 'Receives' in section_names:
+            msg = 'Docstring contains a Receives section but not Yields.'
+            raise ValueError(msg)
+
+        for (section, content) in sections:
+            if not section.startswith('..'):
+                section = (s.capitalize() for s in section.split(' '))
+                section = ' '.join(section)
+                if self.get(section):
+                    self._error_location("The section %s appears twice"
+                                         % section)
+
+            if section in ('Parameters', 'Other Parameters', 'Attributes',
+                           'Methods'):
+                self[section] = self._parse_param_list(content)
+            elif section in ('Returns', 'Yields', 'Raises', 'Warns',
+                             'Receives'):
+                self[section] = self._parse_param_list(
+                    content, single_element_is_type=True)
+            elif section.startswith('.. index::'):
+                self['index'] = self._parse_index(section, content)
+            elif section == 'See Also':
+                self['See Also'] = self._parse_see_also(content)
+            else:
+                self[section] = content
+
+    def _error_location(self, msg, error=True):
+        if hasattr(self, '_obj'):
+            # we know where the docs came from:
+            try:
+                filename = inspect.getsourcefile(self._obj)
+            except TypeError:
+                filename = None
+            msg = msg + (f" in the docstring of {self._obj} in {filename}.")
+        if error:
+            raise ValueError(msg)
+        else:
+            warn(msg, stacklevel=3)
+
+    # string conversion routines
+
+    def _str_header(self, name, symbol='-'):
+        return [name, len(name)*symbol]
+
+    def _str_indent(self, doc, indent=4):
+        out = []
+        for line in doc:
+            out += [' '*indent + line]
+        return out
+
+    def _str_signature(self):
+        if self['Signature']:
+            return [self['Signature'].replace('*', r'\*')] + ['']
+        else:
+            return ['']
+
+    def _str_summary(self):
+        if self['Summary']:
+            return self['Summary'] + ['']
+        else:
+            return []
+
+    def _str_extended_summary(self):
+        if self['Extended Summary']:
+            return self['Extended Summary'] + ['']
+        else:
+            return []
+
+    def _str_param_list(self, name):
+        out = []
+        if self[name]:
+            out += self._str_header(name)
+            for param in self[name]:
+                parts = []
+                if param.name:
+                    parts.append(param.name)
+                if param.type:
+                    parts.append(param.type)
+                out += [' : '.join(parts)]
+                if param.desc and ''.join(param.desc).strip():
+                    out += self._str_indent(param.desc)
+            out += ['']
+        return out
+
+    def _str_section(self, name):
+        out = []
+        if self[name]:
+            out += self._str_header(name)
+            out += self[name]
+            out += ['']
+        return out
+
+    def _str_see_also(self, func_role):
+        if not self['See Also']:
+            return []
+        out = []
+        out += self._str_header("See Also")
+        out += ['']
+        last_had_desc = True
+        for funcs, desc in self['See Also']:
+            assert isinstance(funcs, list)
+            links = []
+            for func, role in funcs:
+                if role:
+                    link = f':{role}:`{func}`'
+                elif func_role:
+                    link = f':{func_role}:`{func}`'
+                else:
+                    link = "`%s`_" % func
+                links.append(link)
+            link = ', '.join(links)
+            out += [link]
+            if desc:
+                out += self._str_indent([' '.join(desc)])
+                last_had_desc = True
+            else:
+                last_had_desc = False
+                out += self._str_indent([self.empty_description])
+
+        if last_had_desc:
+            out += ['']
+        out += ['']
+        return out
+
+    def _str_index(self):
+        idx = self['index']
+        out = []
+        output_index = False
+        default_index = idx.get('default', '')
+        if default_index:
+            output_index = True
+        out += ['.. index:: %s' % default_index]
+        for section, references in idx.items():
+            if section == 'default':
+                continue
+            output_index = True
+            out += ['   :{}: {}'.format(section, ', '.join(references))]
+        if output_index:
+            return out
+        else:
+            return ''
+
+    def __str__(self, func_role=''):
+        out = []
+        out += self._str_signature()
+        out += self._str_summary()
+        out += self._str_extended_summary()
+        for param_list in ('Parameters', 'Returns', 'Yields', 'Receives',
+                           'Other Parameters', 'Raises', 'Warns'):
+            out += self._str_param_list(param_list)
+        out += self._str_section('Warnings')
+        out += self._str_see_also(func_role)
+        for s in ('Notes', 'References', 'Examples'):
+            out += self._str_section(s)
+        for param_list in ('Attributes', 'Methods'):
+            out += self._str_param_list(param_list)
+        out += self._str_index()
+        return '\n'.join(out)
+
+
+def indent(str, indent=4):
+    indent_str = ' '*indent
+    if str is None:
+        return indent_str
+    lines = str.split('\n')
+    return '\n'.join(indent_str + l for l in lines)
+
+
+def dedent_lines(lines):
+    """Deindent a list of lines maximally"""
+    return textwrap.dedent("\n".join(lines)).split("\n")
+
+
+def header(text, style='-'):
+    return text + '\n' + style*len(text) + '\n'
+
+
+class FunctionDoc(NumpyDocString):
+    def __init__(self, func, role='func', doc=None, config={}):
+        self._f = func
+        self._role = role  # e.g. "func" or "meth"
+
+        if doc is None:
+            if func is None:
+                raise ValueError("No function or docstring given")
+            doc = inspect.getdoc(func) or ''
+        NumpyDocString.__init__(self, doc, config)
+
+    def get_func(self):
+        func_name = getattr(self._f, '__name__', self.__class__.__name__)
+        if inspect.isclass(self._f):
+            func = getattr(self._f, '__call__', self._f.__init__)
+        else:
+            func = self._f
+        return func, func_name
+
+    def __str__(self):
+        out = ''
+
+        func, func_name = self.get_func()
+
+        roles = {'func': 'function',
+                 'meth': 'method'}
+
+        if self._role:
+            if self._role not in roles:
+                print("Warning: invalid role %s" % self._role)
+            out += '.. {}:: {}\n    \n\n'.format(roles.get(self._role, ''),
+                                             func_name)
+
+        out += super().__str__(func_role=self._role)
+        return out
+
+
+class ClassDoc(NumpyDocString):
+
+    extra_public_methods = ['__call__']
+
+    def __init__(self, cls, doc=None, modulename='', func_doc=FunctionDoc,
+                 config={}):
+        if not inspect.isclass(cls) and cls is not None:
+            raise ValueError("Expected a class or None, but got %r" % cls)
+        self._cls = cls
+
+        if 'sphinx' in sys.modules:
+            from sphinx.ext.autodoc import ALL
+        else:
+            ALL = object()
+
+        self.show_inherited_members = config.get(
+                    'show_inherited_class_members', True)
+
+        if modulename and not modulename.endswith('.'):
+            modulename += '.'
+        self._mod = modulename
+
+        if doc is None:
+            if cls is None:
+                raise ValueError("No class or documentation string given")
+            doc = pydoc.getdoc(cls)
+
+        NumpyDocString.__init__(self, doc)
+
+        _members = config.get('members', [])
+        if _members is ALL:
+            _members = None
+        _exclude = config.get('exclude-members', [])
+
+        if config.get('show_class_members', True) and _exclude is not ALL:
+            def splitlines_x(s):
+                if not s:
+                    return []
+                else:
+                    return s.splitlines()
+            for field, items in [('Methods', self.methods),
+                                 ('Attributes', self.properties)]:
+                if not self[field]:
+                    doc_list = []
+                    for name in sorted(items):
+                        if (name in _exclude or
+                                (_members and name not in _members)):
+                            continue
+                        try:
+                            doc_item = pydoc.getdoc(getattr(self._cls, name))
+                            doc_list.append(
+                                Parameter(name, '', splitlines_x(doc_item)))
+                        except AttributeError:
+                            pass  # method doesn't exist
+                    self[field] = doc_list
+
+    @property
+    def methods(self):
+        if self._cls is None:
+            return []
+        return [name for name, func in inspect.getmembers(self._cls)
+                if ((not name.startswith('_')
+                     or name in self.extra_public_methods)
+                    and isinstance(func, Callable)
+                    and self._is_show_member(name))]
+
+    @property
+    def properties(self):
+        if self._cls is None:
+            return []
+        return [name for name, func in inspect.getmembers(self._cls)
+                if (not name.startswith('_') and
+                    (func is None or isinstance(func, property) or
+                     inspect.isdatadescriptor(func))
+                    and self._is_show_member(name))]
+
+    def _is_show_member(self, name):
+        if self.show_inherited_members:
+            return True  # show all class members
+        if name not in self._cls.__dict__:
+            return False  # class member is inherited, we do not show it
+        return True

parrot/lib/python3.10/site-packages/scipy/_lib/_elementwise_iterative_method.py

@@ -0,0 +1,348 @@
+# `_elementwise_iterative_method.py` includes tools for writing functions that
+# - are vectorized to work elementwise on arrays,
+# - implement non-trivial, iterative algorithms with a callback interface, and
+# - return rich objects with iteration count, termination status, etc.
+#
+# Examples include:
+# `scipy.optimize._chandrupatla._chandrupatla for scalar rootfinding,
+# `scipy.optimize._chandrupatla._chandrupatla_minimize for scalar minimization,
+# `scipy.optimize._differentiate._differentiate for numerical differentiation,
+# `scipy.optimize._bracket._bracket_root for finding rootfinding brackets,
+# `scipy.optimize._bracket._bracket_minimize for finding minimization brackets,
+# `scipy.integrate._tanhsinh._tanhsinh` for numerical quadrature.
+
+import math
+import numpy as np
+from ._util import _RichResult, _call_callback_maybe_halt
+from ._array_api import array_namespace, size as xp_size
+
+_ESIGNERR = -1
+_ECONVERR = -2
+_EVALUEERR = -3
+_ECALLBACK = -4
+_EINPUTERR = -5
+_ECONVERGED = 0
+_EINPROGRESS = 1
+
+def _initialize(func, xs, args, complex_ok=False, preserve_shape=None):
+    """Initialize abscissa, function, and args arrays for elementwise function
+
+    Parameters
+    ----------
+    func : callable
+        An elementwise function with signature
+
+            func(x: ndarray, *args) -> ndarray
+
+        where each element of ``x`` is a finite real and ``args`` is a tuple,
+        which may contain an arbitrary number of arrays that are broadcastable
+        with ``x``.
+    xs : tuple of arrays
+        Finite real abscissa arrays. Must be broadcastable.
+    args : tuple, optional
+        Additional positional arguments to be passed to `func`.
+    preserve_shape : bool, default:False
+        When ``preserve_shape=False`` (default), `func` may be passed
+        arguments of any shape; `_scalar_optimization_loop` is permitted
+        to reshape and compress arguments at will. When
+        ``preserve_shape=False``, arguments passed to `func` must have shape
+        `shape` or ``shape + (n,)``, where ``n`` is any integer.
+
+    Returns
+    -------
+    xs, fs, args : tuple of arrays
+        Broadcasted, writeable, 1D abscissa and function value arrays (or
+        NumPy floats, if appropriate). The dtypes of the `xs` and `fs` are
+        `xfat`; the dtype of the `args` are unchanged.
+    shape : tuple of ints
+        Original shape of broadcasted arrays.
+    xfat : NumPy dtype
+        Result dtype of abscissae, function values, and args determined using
+        `np.result_type`, except integer types are promoted to `np.float64`.
+
+    Raises
+    ------
+    ValueError
+        If the result dtype is not that of a real scalar
+
+    Notes
+    -----
+    Useful for initializing the input of SciPy functions that accept
+    an elementwise callable, abscissae, and arguments; e.g.
+    `scipy.optimize._chandrupatla`.
+    """
+    nx = len(xs)
+    xp = array_namespace(*xs)
+
+    # Try to preserve `dtype`, but we need to ensure that the arguments are at
+    # least floats before passing them into the function; integers can overflow
+    # and cause failure.
+    # There might be benefit to combining the `xs` into a single array and
+    # calling `func` once on the combined array. For now, keep them separate.
+    xas = xp.broadcast_arrays(*xs, *args)  # broadcast and rename
+    xat = xp.result_type(*[xa.dtype for xa in xas])
+    xat = xp.asarray(1.).dtype if xp.isdtype(xat, "integral") else xat
+    xs, args = xas[:nx], xas[nx:]
+    xs = [xp.asarray(x, dtype=xat) for x in xs]  # use copy=False when implemented
+    fs = [xp.asarray(func(x, *args)) for x in xs]
+    shape = xs[0].shape
+    fshape = fs[0].shape
+
+    if preserve_shape:
+        # bind original shape/func now to avoid late-binding gotcha
+        def func(x, *args, shape=shape, func=func,  **kwargs):
+            i = (0,)*(len(fshape) - len(shape))
+            return func(x[i], *args, **kwargs)
+        shape = np.broadcast_shapes(fshape, shape)  # just shapes; use of NumPy OK
+        xs = [xp.broadcast_to(x, shape) for x in xs]
+        args = [xp.broadcast_to(arg, shape) for arg in args]
+
+    message = ("The shape of the array returned by `func` must be the same as "
+               "the broadcasted shape of `x` and all other `args`.")
+    if preserve_shape is not None:  # only in tanhsinh for now
+        message = f"When `preserve_shape=False`, {message.lower()}"
+    shapes_equal = [f.shape == shape for f in fs]
+    if not all(shapes_equal):  # use Python all to reduce overhead
+        raise ValueError(message)
+
+    # These algorithms tend to mix the dtypes of the abscissae and function
+    # values, so figure out what the result will be and convert them all to
+    # that type from the outset.
+    xfat = xp.result_type(*([f.dtype for f in fs] + [xat]))
+    if not complex_ok and not xp.isdtype(xfat, "real floating"):
+        raise ValueError("Abscissae and function output must be real numbers.")
+    xs = [xp.asarray(x, dtype=xfat, copy=True) for x in xs]
+    fs = [xp.asarray(f, dtype=xfat, copy=True) for f in fs]
+
+    # To ensure that we can do indexing, we'll work with at least 1d arrays,
+    # but remember the appropriate shape of the output.
+    xs = [xp.reshape(x, (-1,)) for x in xs]
+    fs = [xp.reshape(f, (-1,)) for f in fs]
+    args = [xp.reshape(xp.asarray(arg, copy=True), (-1,)) for arg in args]
+    return func, xs, fs, args, shape, xfat, xp
+
+
+def _loop(work, callback, shape, maxiter, func, args, dtype, pre_func_eval,
+          post_func_eval, check_termination, post_termination_check,
+          customize_result, res_work_pairs, xp, preserve_shape=False):
+    """Main loop of a vectorized scalar optimization algorithm
+
+    Parameters
+    ----------
+    work : _RichResult
+        All variables that need to be retained between iterations. Must
+        contain attributes `nit`, `nfev`, and `success`
+    callback : callable
+        User-specified callback function
+    shape : tuple of ints
+        The shape of all output arrays
+    maxiter :
+        Maximum number of iterations of the algorithm
+    func : callable
+        The user-specified callable that is being optimized or solved
+    args : tuple
+        Additional positional arguments to be passed to `func`.
+    dtype : NumPy dtype
+        The common dtype of all abscissae and function values
+    pre_func_eval : callable
+        A function that accepts `work` and returns `x`, the active elements
+        of `x` at which `func` will be evaluated. May modify attributes
+        of `work` with any algorithmic steps that need to happen
+         at the beginning of an iteration, before `func` is evaluated,
+    post_func_eval : callable
+        A function that accepts `x`, `func(x)`, and `work`. May modify
+        attributes of `work` with any algorithmic steps that need to happen
+         in the middle of an iteration, after `func` is evaluated but before
+         the termination check.
+    check_termination : callable
+        A function that accepts `work` and returns `stop`, a boolean array
+        indicating which of the active elements have met a termination
+        condition.
+    post_termination_check : callable
+        A function that accepts `work`. May modify `work` with any algorithmic
+        steps that need to happen after the termination check and before the
+        end of the iteration.
+    customize_result : callable
+        A function that accepts `res` and `shape` and returns `shape`. May
+        modify `res` (in-place) according to preferences (e.g. rearrange
+        elements between attributes) and modify `shape` if needed.
+    res_work_pairs : list of (str, str)
+        Identifies correspondence between attributes of `res` and attributes
+        of `work`; i.e., attributes of active elements of `work` will be
+        copied to the appropriate indices of `res` when appropriate. The order
+        determines the order in which _RichResult attributes will be
+        pretty-printed.
+
+    Returns
+    -------
+    res : _RichResult
+        The final result object
+
+    Notes
+    -----
+    Besides providing structure, this framework provides several important
+    services for a vectorized optimization algorithm.
+
+    - It handles common tasks involving iteration count, function evaluation
+      count, a user-specified callback, and associated termination conditions.
+    - It compresses the attributes of `work` to eliminate unnecessary
+      computation on elements that have already converged.
+
+    """
+    if xp is None:
+        raise NotImplementedError("Must provide xp.")
+
+    cb_terminate = False
+
+    # Initialize the result object and active element index array
+    n_elements = math.prod(shape)
+    active = xp.arange(n_elements)  # in-progress element indices
+    res_dict = {i: xp.zeros(n_elements, dtype=dtype) for i, j in res_work_pairs}
+    res_dict['success'] = xp.zeros(n_elements, dtype=xp.bool)
+    res_dict['status'] = xp.full(n_elements, _EINPROGRESS, dtype=xp.int32)
+    res_dict['nit'] = xp.zeros(n_elements, dtype=xp.int32)
+    res_dict['nfev'] = xp.zeros(n_elements, dtype=xp.int32)
+    res = _RichResult(res_dict)
+    work.args = args
+
+    active = _check_termination(work, res, res_work_pairs, active,
+                                check_termination, preserve_shape, xp)
+
+    if callback is not None:
+        temp = _prepare_result(work, res, res_work_pairs, active, shape,
+                               customize_result, preserve_shape, xp)
+        if _call_callback_maybe_halt(callback, temp):
+            cb_terminate = True
+
+    while work.nit < maxiter and xp_size(active) and not cb_terminate and n_elements:
+        x = pre_func_eval(work)
+
+        if work.args and work.args[0].ndim != x.ndim:
+            # `x` always starts as 1D. If the SciPy function that uses
+            # _loop added dimensions to `x`, we need to
+            # add them to the elements of `args`.
+            args = []
+            for arg in work.args:
+                n_new_dims = x.ndim - arg.ndim
+                new_shape = arg.shape + (1,)*n_new_dims
+                args.append(xp.reshape(arg, new_shape))
+            work.args = args
+
+        x_shape = x.shape
+        if preserve_shape:
+            x = xp.reshape(x, (shape + (-1,)))
+        f = func(x, *work.args)
+        f = xp.asarray(f, dtype=dtype)
+        if preserve_shape:
+            x = xp.reshape(x, x_shape)
+            f = xp.reshape(f, x_shape)
+        work.nfev += 1 if x.ndim == 1 else x.shape[-1]
+
+        post_func_eval(x, f, work)
+
+        work.nit += 1
+        active = _check_termination(work, res, res_work_pairs, active,
+                                    check_termination, preserve_shape, xp)
+
+        if callback is not None:
+            temp = _prepare_result(work, res, res_work_pairs, active, shape,
+                                   customize_result, preserve_shape, xp)
+            if _call_callback_maybe_halt(callback, temp):
+                cb_terminate = True
+                break
+        if xp_size(active) == 0:
+            break
+
+        post_termination_check(work)
+
+    work.status[:] = _ECALLBACK if cb_terminate else _ECONVERR
+    return _prepare_result(work, res, res_work_pairs, active, shape,
+                           customize_result, preserve_shape, xp)
+
+
+def _check_termination(work, res, res_work_pairs, active, check_termination,
+                       preserve_shape, xp):
+    # Checks termination conditions, updates elements of `res` with
+    # corresponding elements of `work`, and compresses `work`.
+
+    stop = check_termination(work)
+
+    if xp.any(stop):
+        # update the active elements of the result object with the active
+        # elements for which a termination condition has been met
+        _update_active(work, res, res_work_pairs, active, stop, preserve_shape, xp)
+
+        if preserve_shape:
+            stop = stop[active]
+
+        proceed = ~stop
+        active = active[proceed]
+
+        if not preserve_shape:
+            # compress the arrays to avoid unnecessary computation
+            for key, val in work.items():
+                # Need to find a better way than these try/excepts
+                # Somehow need to keep compressible numerical args separate
+                if key == 'args':
+                    continue
+                try:
+                    work[key] = val[proceed]
+                except (IndexError, TypeError, KeyError):  # not a compressible array
+                    work[key] = val
+            work.args = [arg[proceed] for arg in work.args]
+
+    return active
+
+
+def _update_active(work, res, res_work_pairs, active, mask, preserve_shape, xp):
+    # Update `active` indices of the arrays in result object `res` with the
+    # contents of the scalars and arrays in `update_dict`. When provided,
+    # `mask` is a boolean array applied both to the arrays in `update_dict`
+    # that are to be used and to the arrays in `res` that are to be updated.
+    update_dict = {key1: work[key2] for key1, key2 in res_work_pairs}
+    update_dict['success'] = work.status == 0
+
+    if mask is not None:
+        if preserve_shape:
+            active_mask = xp.zeros_like(mask)
+            active_mask[active] = 1
+            active_mask = active_mask & mask
+            for key, val in update_dict.items():
+                try:
+                    res[key][active_mask] = val[active_mask]
+                except (IndexError, TypeError, KeyError):
+                    res[key][active_mask] = val
+        else:
+            active_mask = active[mask]
+            for key, val in update_dict.items():
+                try:
+                    res[key][active_mask] = val[mask]
+                except (IndexError, TypeError, KeyError):
+                    res[key][active_mask] = val
+    else:
+        for key, val in update_dict.items():
+            if preserve_shape:
+                try:
+                    val = val[active]
+                except (IndexError, TypeError, KeyError):
+                    pass
+            res[key][active] = val
+
+
+def _prepare_result(work, res, res_work_pairs, active, shape, customize_result,
+                    preserve_shape, xp):
+    # Prepare the result object `res` by creating a copy, copying the latest
+    # data from work, running the provided result customization function,
+    # and reshaping the data to the original shapes.
+    res = res.copy()
+    _update_active(work, res, res_work_pairs, active, None, preserve_shape, xp)
+
+    shape = customize_result(res, shape)
+
+    for key, val in res.items():
+        # this looks like it won't work for xp != np if val is not numeric
+        temp = xp.reshape(val, shape)
+        res[key] = temp[()] if temp.ndim == 0 else temp
+
+    res['_order_keys'] = ['success'] + [i for i, j in res_work_pairs]
+    return _RichResult(**res)

parrot/lib/python3.10/site-packages/scipy/_lib/_finite_differences.py

@@ -0,0 +1,145 @@
+from numpy import arange, newaxis, hstack, prod, array
+
+
+def _central_diff_weights(Np, ndiv=1):
+    """
+    Return weights for an Np-point central derivative.
+
+    Assumes equally-spaced function points.
+
+    If weights are in the vector w, then
+    derivative is w[0] * f(x-ho*dx) + ... + w[-1] * f(x+h0*dx)
+
+    Parameters
+    ----------
+    Np : int
+        Number of points for the central derivative.
+    ndiv : int, optional
+        Number of divisions. Default is 1.
+
+    Returns
+    -------
+    w : ndarray
+        Weights for an Np-point central derivative. Its size is `Np`.
+
+    Notes
+    -----
+    Can be inaccurate for a large number of points.
+
+    Examples
+    --------
+    We can calculate a derivative value of a function.
+
+    >>> def f(x):
+    ...     return 2 * x**2 + 3
+    >>> x = 3.0 # derivative point
+    >>> h = 0.1 # differential step
+    >>> Np = 3 # point number for central derivative
+    >>> weights = _central_diff_weights(Np) # weights for first derivative
+    >>> vals = [f(x + (i - Np/2) * h) for i in range(Np)]
+    >>> sum(w * v for (w, v) in zip(weights, vals))/h
+    11.79999999999998
+
+    This value is close to the analytical solution:
+    f'(x) = 4x, so f'(3) = 12
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Finite_difference
+
+    """
+    if Np < ndiv + 1:
+        raise ValueError(
+            "Number of points must be at least the derivative order + 1."
+        )
+    if Np % 2 == 0:
+        raise ValueError("The number of points must be odd.")
+    from scipy import linalg
+
+    ho = Np >> 1
+    x = arange(-ho, ho + 1.0)
+    x = x[:, newaxis]
+    X = x**0.0
+    for k in range(1, Np):
+        X = hstack([X, x**k])
+    w = prod(arange(1, ndiv + 1), axis=0) * linalg.inv(X)[ndiv]
+    return w
+
+
+def _derivative(func, x0, dx=1.0, n=1, args=(), order=3):
+    """
+    Find the nth derivative of a function at a point.
+
+    Given a function, use a central difference formula with spacing `dx` to
+    compute the nth derivative at `x0`.
+
+    Parameters
+    ----------
+    func : function
+        Input function.
+    x0 : float
+        The point at which the nth derivative is found.
+    dx : float, optional
+        Spacing.
+    n : int, optional
+        Order of the derivative. Default is 1.
+    args : tuple, optional
+        Arguments
+    order : int, optional
+        Number of points to use, must be odd.
+
+    Notes
+    -----
+    Decreasing the step size too small can result in round-off error.
+
+    Examples
+    --------
+    >>> def f(x):
+    ...     return x**3 + x**2
+    >>> _derivative(f, 1.0, dx=1e-6)
+    4.9999999999217337
+
+    """
+    if order < n + 1:
+        raise ValueError(
+            "'order' (the number of points used to compute the derivative), "
+            "must be at least the derivative order 'n' + 1."
+        )
+    if order % 2 == 0:
+        raise ValueError(
+            "'order' (the number of points used to compute the derivative) "
+            "must be odd."
+        )
+    # pre-computed for n=1 and 2 and low-order for speed.
+    if n == 1:
+        if order == 3:
+            weights = array([-1, 0, 1]) / 2.0
+        elif order == 5:
+            weights = array([1, -8, 0, 8, -1]) / 12.0
+        elif order == 7:
+            weights = array([-1, 9, -45, 0, 45, -9, 1]) / 60.0
+        elif order == 9:
+            weights = array([3, -32, 168, -672, 0, 672, -168, 32, -3]) / 840.0
+        else:
+            weights = _central_diff_weights(order, 1)
+    elif n == 2:
+        if order == 3:
+            weights = array([1, -2.0, 1])
+        elif order == 5:
+            weights = array([-1, 16, -30, 16, -1]) / 12.0
+        elif order == 7:
+            weights = array([2, -27, 270, -490, 270, -27, 2]) / 180.0
+        elif order == 9:
+            weights = (
+                array([-9, 128, -1008, 8064, -14350, 8064, -1008, 128, -9])
+                / 5040.0
+            )
+        else:
+            weights = _central_diff_weights(order, 2)
+    else:
+        weights = _central_diff_weights(order, n)
+    val = 0.0
+    ho = order >> 1
+    for k in range(order):
+        val += weights[k] * func(x0 + (k - ho) * dx, *args)
+    return val / prod((dx,) * n, axis=0)

parrot/lib/python3.10/site-packages/scipy/_lib/_threadsafety.py

@@ -0,0 +1,58 @@
+import threading
+
+import scipy._lib.decorator
+
+
+__all__ = ['ReentrancyError', 'ReentrancyLock', 'non_reentrant']
+
+
+class ReentrancyError(RuntimeError):
+    pass
+
+
+class ReentrancyLock:
+    """
+    Threading lock that raises an exception for reentrant calls.
+
+    Calls from different threads are serialized, and nested calls from the
+    same thread result to an error.
+
+    The object can be used as a context manager or to decorate functions
+    via the decorate() method.
+
+    """
+
+    def __init__(self, err_msg):
+        self._rlock = threading.RLock()
+        self._entered = False
+        self._err_msg = err_msg
+
+    def __enter__(self):
+        self._rlock.acquire()
+        if self._entered:
+            self._rlock.release()
+            raise ReentrancyError(self._err_msg)
+        self._entered = True
+
+    def __exit__(self, type, value, traceback):
+        self._entered = False
+        self._rlock.release()
+
+    def decorate(self, func):
+        def caller(func, *a, **kw):
+            with self:
+                return func(*a, **kw)
+        return scipy._lib.decorator.decorate(func, caller)
+
+
+def non_reentrant(err_msg=None):
+    """
+    Decorate a function with a threading lock and prevent reentrant calls.
+    """
+    def decorator(func):
+        msg = err_msg
+        if msg is None:
+            msg = "%s is not re-entrant" % func.__name__
+        lock = ReentrancyLock(msg)
+        return lock.decorate(func)
+    return decorator

parrot/lib/python3.10/site-packages/scipy/_lib/deprecation.py

@@ -0,0 +1,239 @@
+from inspect import Parameter, signature
+import functools
+import warnings
+from importlib import import_module
+
+
+__all__ = ["_deprecated"]
+
+
+# Object to use as default value for arguments to be deprecated. This should
+# be used over 'None' as the user could parse 'None' as a positional argument
+_NoValue = object()
+
+def _sub_module_deprecation(*, sub_package, module, private_modules, all,
+                            attribute, correct_module=None):
+    """Helper function for deprecating modules that are public but were
+    intended to be private.
+
+    Parameters
+    ----------
+    sub_package : str
+        Subpackage the module belongs to eg. stats
+    module : str
+        Public but intended private module to deprecate
+    private_modules : list
+        Private replacement(s) for `module`; should contain the
+        content of ``all``, possibly spread over several modules.
+    all : list
+        ``__all__`` belonging to `module`
+    attribute : str
+        The attribute in `module` being accessed
+    correct_module : str, optional
+        Module in `sub_package` that `attribute` should be imported from.
+        Default is that `attribute` should be imported from ``scipy.sub_package``.
+    """
+    if correct_module is not None:
+        correct_import = f"scipy.{sub_package}.{correct_module}"
+    else:
+        correct_import = f"scipy.{sub_package}"
+
+    if attribute not in all:
+        raise AttributeError(
+            f"`scipy.{sub_package}.{module}` has no attribute `{attribute}`; "
+            f"furthermore, `scipy.{sub_package}.{module}` is deprecated "
+            f"and will be removed in SciPy 2.0.0."
+        )
+
+    attr = getattr(import_module(correct_import), attribute, None)
+
+    if attr is not None:
+        message = (
+            f"Please import `{attribute}` from the `{correct_import}` namespace; "
+            f"the `scipy.{sub_package}.{module}` namespace is deprecated "
+            f"and will be removed in SciPy 2.0.0."
+        )
+    else:
+        message = (
+            f"`scipy.{sub_package}.{module}.{attribute}` is deprecated along with "
+            f"the `scipy.{sub_package}.{module}` namespace. "
+            f"`scipy.{sub_package}.{module}.{attribute}` will be removed "
+            f"in SciPy 1.14.0, and the `scipy.{sub_package}.{module}` namespace "
+            f"will be removed in SciPy 2.0.0."
+        )
+
+    warnings.warn(message, category=DeprecationWarning, stacklevel=3)
+
+    for module in private_modules:
+        try:
+            return getattr(import_module(f"scipy.{sub_package}.{module}"), attribute)
+        except AttributeError as e:
+            # still raise an error if the attribute isn't in any of the expected
+            # private modules
+            if module == private_modules[-1]:
+                raise e
+            continue
+    
+
+def _deprecated(msg, stacklevel=2):
+    """Deprecate a function by emitting a warning on use."""
+    def wrap(fun):
+        if isinstance(fun, type):
+            warnings.warn(
+                f"Trying to deprecate class {fun!r}",
+                category=RuntimeWarning, stacklevel=2)
+            return fun
+
+        @functools.wraps(fun)
+        def call(*args, **kwargs):
+            warnings.warn(msg, category=DeprecationWarning,
+                          stacklevel=stacklevel)
+            return fun(*args, **kwargs)
+        call.__doc__ = fun.__doc__
+        return call
+
+    return wrap
+
+
+class _DeprecationHelperStr:
+    """
+    Helper class used by deprecate_cython_api
+    """
+    def __init__(self, content, message):
+        self._content = content
+        self._message = message
+
+    def __hash__(self):
+        return hash(self._content)
+
+    def __eq__(self, other):
+        res = (self._content == other)
+        if res:
+            warnings.warn(self._message, category=DeprecationWarning,
+                          stacklevel=2)
+        return res
+
+
+def deprecate_cython_api(module, routine_name, new_name=None, message=None):
+    """
+    Deprecate an exported cdef function in a public Cython API module.
+
+    Only functions can be deprecated; typedefs etc. cannot.
+
+    Parameters
+    ----------
+    module : module
+        Public Cython API module (e.g. scipy.linalg.cython_blas).
+    routine_name : str
+        Name of the routine to deprecate. May also be a fused-type
+        routine (in which case its all specializations are deprecated).
+    new_name : str
+        New name to include in the deprecation warning message
+    message : str
+        Additional text in the deprecation warning message
+
+    Examples
+    --------
+    Usually, this function would be used in the top-level of the
+    module ``.pyx`` file:
+
+    >>> from scipy._lib.deprecation import deprecate_cython_api
+    >>> import scipy.linalg.cython_blas as mod
+    >>> deprecate_cython_api(mod, "dgemm", "dgemm_new",
+    ...                      message="Deprecated in Scipy 1.5.0")
+    >>> del deprecate_cython_api, mod
+
+    After this, Cython modules that use the deprecated function emit a
+    deprecation warning when they are imported.
+
+    """
+    old_name = f"{module.__name__}.{routine_name}"
+
+    if new_name is None:
+        depdoc = "`%s` is deprecated!" % old_name
+    else:
+        depdoc = f"`{old_name}` is deprecated, use `{new_name}` instead!"
+
+    if message is not None:
+        depdoc += "\n" + message
+
+    d = module.__pyx_capi__
+
+    # Check if the function is a fused-type function with a mangled name
+    j = 0
+    has_fused = False
+    while True:
+        fused_name = f"__pyx_fuse_{j}{routine_name}"
+        if fused_name in d:
+            has_fused = True
+            d[_DeprecationHelperStr(fused_name, depdoc)] = d.pop(fused_name)
+            j += 1
+        else:
+            break
+
+    # If not, apply deprecation to the named routine
+    if not has_fused:
+        d[_DeprecationHelperStr(routine_name, depdoc)] = d.pop(routine_name)
+
+
+# taken from scikit-learn, see
+# https://github.com/scikit-learn/scikit-learn/blob/1.3.0/sklearn/utils/validation.py#L38
+def _deprecate_positional_args(func=None, *, version=None):
+    """Decorator for methods that issues warnings for positional arguments.
+
+    Using the keyword-only argument syntax in pep 3102, arguments after the
+    * will issue a warning when passed as a positional argument.
+
+    Parameters
+    ----------
+    func : callable, default=None
+        Function to check arguments on.
+    version : callable, default=None
+        The version when positional arguments will result in error.
+    """
+    if version is None:
+        msg = "Need to specify a version where signature will be changed"
+        raise ValueError(msg)
+
+    def _inner_deprecate_positional_args(f):
+        sig = signature(f)
+        kwonly_args = []
+        all_args = []
+
+        for name, param in sig.parameters.items():
+            if param.kind == Parameter.POSITIONAL_OR_KEYWORD:
+                all_args.append(name)
+            elif param.kind == Parameter.KEYWORD_ONLY:
+                kwonly_args.append(name)
+
+        @functools.wraps(f)
+        def inner_f(*args, **kwargs):
+            extra_args = len(args) - len(all_args)
+            if extra_args <= 0:
+                return f(*args, **kwargs)
+
+            # extra_args > 0
+            args_msg = [
+                f"{name}={arg}"
+                for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:])
+            ]
+            args_msg = ", ".join(args_msg)
+            warnings.warn(
+                (
+                    f"You are passing {args_msg} as a positional argument. "
+                    "Please change your invocation to use keyword arguments. "
+                    f"From SciPy {version}, passing these as positional "
+                    "arguments will result in an error."
+                ),
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            kwargs.update(zip(sig.parameters, args))
+            return f(**kwargs)
+
+        return inner_f
+
+    if func is not None:
+        return _inner_deprecate_positional_args(func)
+
+    return _inner_deprecate_positional_args

parrot/lib/python3.10/site-packages/scipy/constants/_constants.py

@@ -0,0 +1,368 @@
+"""
+Collection of physical constants and conversion factors.
+
+Most constants are in SI units, so you can do
+print '10 mile per minute is', 10*mile/minute, 'm/s or', 10*mile/(minute*knot), 'knots'
+
+The list is not meant to be comprehensive, but just convenient for everyday use.
+"""
+
+from __future__ import annotations
+
+import math as _math
+from typing import TYPE_CHECKING, Any
+
+from ._codata import value as _cd
+
+if TYPE_CHECKING:
+    import numpy.typing as npt
+
+from scipy._lib._array_api import array_namespace, _asarray
+
+
+"""
+BasSw 2006
+physical constants: imported from CODATA
+unit conversion: see e.g., NIST special publication 811
+Use at own risk: double-check values before calculating your Mars orbit-insertion burn.
+Some constants exist in a few variants, which are marked with suffixes.
+The ones without any suffix should be the most common ones.
+"""
+
+__all__ = [
+    'Avogadro', 'Boltzmann', 'Btu', 'Btu_IT', 'Btu_th', 'G',
+    'Julian_year', 'N_A', 'Planck', 'R', 'Rydberg',
+    'Stefan_Boltzmann', 'Wien', 'acre', 'alpha',
+    'angstrom', 'arcmin', 'arcminute', 'arcsec',
+    'arcsecond', 'astronomical_unit', 'atm',
+    'atmosphere', 'atomic_mass', 'atto', 'au', 'bar',
+    'barrel', 'bbl', 'blob', 'c', 'calorie',
+    'calorie_IT', 'calorie_th', 'carat', 'centi',
+    'convert_temperature', 'day', 'deci', 'degree',
+    'degree_Fahrenheit', 'deka', 'dyn', 'dyne', 'e',
+    'eV', 'electron_mass', 'electron_volt',
+    'elementary_charge', 'epsilon_0', 'erg',
+    'exa', 'exbi', 'femto', 'fermi', 'fine_structure',
+    'fluid_ounce', 'fluid_ounce_US', 'fluid_ounce_imp',
+    'foot', 'g', 'gallon', 'gallon_US', 'gallon_imp',
+    'gas_constant', 'gibi', 'giga', 'golden', 'golden_ratio',
+    'grain', 'gram', 'gravitational_constant', 'h', 'hbar',
+    'hectare', 'hecto', 'horsepower', 'hour', 'hp',
+    'inch', 'k', 'kgf', 'kibi', 'kilo', 'kilogram_force',
+    'kmh', 'knot', 'lambda2nu', 'lb', 'lbf',
+    'light_year', 'liter', 'litre', 'long_ton', 'm_e',
+    'm_n', 'm_p', 'm_u', 'mach', 'mebi', 'mega',
+    'metric_ton', 'micro', 'micron', 'mil', 'mile',
+    'milli', 'minute', 'mmHg', 'mph', 'mu_0', 'nano',
+    'nautical_mile', 'neutron_mass', 'nu2lambda',
+    'ounce', 'oz', 'parsec', 'pebi', 'peta',
+    'pi', 'pico', 'point', 'pound', 'pound_force',
+    'proton_mass', 'psi', 'pt', 'quecto', 'quetta', 'ronna', 'ronto',
+    'short_ton', 'sigma', 'slinch', 'slug', 'speed_of_light',
+    'speed_of_sound', 'stone', 'survey_foot',
+    'survey_mile', 'tebi', 'tera', 'ton_TNT',
+    'torr', 'troy_ounce', 'troy_pound', 'u',
+    'week', 'yard', 'year', 'yobi', 'yocto',
+    'yotta', 'zebi', 'zepto', 'zero_Celsius', 'zetta'
+]
+
+
+# mathematical constants
+pi = _math.pi
+golden = golden_ratio = (1 + _math.sqrt(5)) / 2
+
+# SI prefixes
+quetta = 1e30
+ronna = 1e27
+yotta = 1e24
+zetta = 1e21
+exa = 1e18
+peta = 1e15
+tera = 1e12
+giga = 1e9
+mega = 1e6
+kilo = 1e3
+hecto = 1e2
+deka = 1e1
+deci = 1e-1
+centi = 1e-2
+milli = 1e-3
+micro = 1e-6
+nano = 1e-9
+pico = 1e-12
+femto = 1e-15
+atto = 1e-18
+zepto = 1e-21
+yocto = 1e-24
+ronto = 1e-27
+quecto = 1e-30
+
+# binary prefixes
+kibi = 2**10
+mebi = 2**20
+gibi = 2**30
+tebi = 2**40
+pebi = 2**50
+exbi = 2**60
+zebi = 2**70
+yobi = 2**80
+
+# physical constants
+c = speed_of_light = _cd('speed of light in vacuum')
+mu_0 = _cd('vacuum mag. permeability')
+epsilon_0 = _cd('vacuum electric permittivity')
+h = Planck = _cd('Planck constant')
+hbar = h / (2 * pi)
+G = gravitational_constant = _cd('Newtonian constant of gravitation')
+g = _cd('standard acceleration of gravity')
+e = elementary_charge = _cd('elementary charge')
+R = gas_constant = _cd('molar gas constant')
+alpha = fine_structure = _cd('fine-structure constant')
+N_A = Avogadro = _cd('Avogadro constant')
+k = Boltzmann = _cd('Boltzmann constant')
+sigma = Stefan_Boltzmann = _cd('Stefan-Boltzmann constant')
+Wien = _cd('Wien wavelength displacement law constant')
+Rydberg = _cd('Rydberg constant')
+
+# mass in kg
+gram = 1e-3
+metric_ton = 1e3
+grain = 64.79891e-6
+lb = pound = 7000 * grain  # avoirdupois
+blob = slinch = pound * g / 0.0254  # lbf*s**2/in (added in 1.0.0)
+slug = blob / 12  # lbf*s**2/foot (added in 1.0.0)
+oz = ounce = pound / 16
+stone = 14 * pound
+long_ton = 2240 * pound
+short_ton = 2000 * pound
+
+troy_ounce = 480 * grain  # only for metals / gems
+troy_pound = 12 * troy_ounce
+carat = 200e-6
+
+m_e = electron_mass = _cd('electron mass')
+m_p = proton_mass = _cd('proton mass')
+m_n = neutron_mass = _cd('neutron mass')
+m_u = u = atomic_mass = _cd('atomic mass constant')
+
+# angle in rad
+degree = pi / 180
+arcmin = arcminute = degree / 60
+arcsec = arcsecond = arcmin / 60
+
+# time in second
+minute = 60.0
+hour = 60 * minute
+day = 24 * hour
+week = 7 * day
+year = 365 * day
+Julian_year = 365.25 * day
+
+# length in meter
+inch = 0.0254
+foot = 12 * inch
+yard = 3 * foot
+mile = 1760 * yard
+mil = inch / 1000
+pt = point = inch / 72  # typography
+survey_foot = 1200.0 / 3937
+survey_mile = 5280 * survey_foot
+nautical_mile = 1852.0
+fermi = 1e-15
+angstrom = 1e-10
+micron = 1e-6
+au = astronomical_unit = 149597870700.0
+light_year = Julian_year * c
+parsec = au / arcsec
+
+# pressure in pascal
+atm = atmosphere = _cd('standard atmosphere')
+bar = 1e5
+torr = mmHg = atm / 760
+psi = pound * g / (inch * inch)
+
+# area in meter**2
+hectare = 1e4
+acre = 43560 * foot**2
+
+# volume in meter**3
+litre = liter = 1e-3
+gallon = gallon_US = 231 * inch**3  # US
+# pint = gallon_US / 8
+fluid_ounce = fluid_ounce_US = gallon_US / 128
+bbl = barrel = 42 * gallon_US  # for oil
+
+gallon_imp = 4.54609e-3  # UK
+fluid_ounce_imp = gallon_imp / 160
+
+# speed in meter per second
+kmh = 1e3 / hour
+mph = mile / hour
+# approx value of mach at 15 degrees in 1 atm. Is this a common value?
+mach = speed_of_sound = 340.5
+knot = nautical_mile / hour
+
+# temperature in kelvin
+zero_Celsius = 273.15
+degree_Fahrenheit = 1/1.8  # only for differences
+
+# energy in joule
+eV = electron_volt = elementary_charge  # * 1 Volt
+calorie = calorie_th = 4.184
+calorie_IT = 4.1868
+erg = 1e-7
+Btu_th = pound * degree_Fahrenheit * calorie_th / gram
+Btu = Btu_IT = pound * degree_Fahrenheit * calorie_IT / gram
+ton_TNT = 1e9 * calorie_th
+# Wh = watt_hour
+
+# power in watt
+hp = horsepower = 550 * foot * pound * g
+
+# force in newton
+dyn = dyne = 1e-5
+lbf = pound_force = pound * g
+kgf = kilogram_force = g  # * 1 kg
+
+# functions for conversions that are not linear
+
+
+def convert_temperature(
+    val: npt.ArrayLike,
+    old_scale: str,
+    new_scale: str,
+) -> Any:
+    """
+    Convert from a temperature scale to another one among Celsius, Kelvin,
+    Fahrenheit, and Rankine scales.
+
+    Parameters
+    ----------
+    val : array_like
+        Value(s) of the temperature(s) to be converted expressed in the
+        original scale.
+    old_scale : str
+        Specifies as a string the original scale from which the temperature
+        value(s) will be converted. Supported scales are Celsius ('Celsius',
+        'celsius', 'C' or 'c'), Kelvin ('Kelvin', 'kelvin', 'K', 'k'),
+        Fahrenheit ('Fahrenheit', 'fahrenheit', 'F' or 'f'), and Rankine
+        ('Rankine', 'rankine', 'R', 'r').
+    new_scale : str
+        Specifies as a string the new scale to which the temperature
+        value(s) will be converted. Supported scales are Celsius ('Celsius',
+        'celsius', 'C' or 'c'), Kelvin ('Kelvin', 'kelvin', 'K', 'k'),
+        Fahrenheit ('Fahrenheit', 'fahrenheit', 'F' or 'f'), and Rankine
+        ('Rankine', 'rankine', 'R', 'r').
+
+    Returns
+    -------
+    res : float or array of floats
+        Value(s) of the converted temperature(s) expressed in the new scale.
+
+    Notes
+    -----
+    .. versionadded:: 0.18.0
+
+    Examples
+    --------
+    >>> from scipy.constants import convert_temperature
+    >>> import numpy as np
+    >>> convert_temperature(np.array([-40, 40]), 'Celsius', 'Kelvin')
+    array([ 233.15,  313.15])
+
+    """
+    xp = array_namespace(val)
+    _val = _asarray(val, xp=xp, subok=True)
+    # Convert from `old_scale` to Kelvin
+    if old_scale.lower() in ['celsius', 'c']:
+        tempo = _val + zero_Celsius
+    elif old_scale.lower() in ['kelvin', 'k']:
+        tempo = _val
+    elif old_scale.lower() in ['fahrenheit', 'f']:
+        tempo = (_val - 32) * 5 / 9 + zero_Celsius
+    elif old_scale.lower() in ['rankine', 'r']:
+        tempo = _val * 5 / 9
+    else:
+        raise NotImplementedError(f"{old_scale=} is unsupported: supported scales "
+                                   "are Celsius, Kelvin, Fahrenheit, and "
+                                   "Rankine")
+    # and from Kelvin to `new_scale`.
+    if new_scale.lower() in ['celsius', 'c']:
+        res = tempo - zero_Celsius
+    elif new_scale.lower() in ['kelvin', 'k']:
+        res = tempo
+    elif new_scale.lower() in ['fahrenheit', 'f']:
+        res = (tempo - zero_Celsius) * 9 / 5 + 32
+    elif new_scale.lower() in ['rankine', 'r']:
+        res = tempo * 9 / 5
+    else:
+        raise NotImplementedError(f"{new_scale=} is unsupported: supported "
+                                   "scales are 'Celsius', 'Kelvin', "
+                                   "'Fahrenheit', and 'Rankine'")
+
+    return res
+
+
+# optics
+
+
+def lambda2nu(lambda_: npt.ArrayLike) -> Any:
+    """
+    Convert wavelength to optical frequency
+
+    Parameters
+    ----------
+    lambda_ : array_like
+        Wavelength(s) to be converted.
+
+    Returns
+    -------
+    nu : float or array of floats
+        Equivalent optical frequency.
+
+    Notes
+    -----
+    Computes ``nu = c / lambda`` where c = 299792458.0, i.e., the
+    (vacuum) speed of light in meters/second.
+
+    Examples
+    --------
+    >>> from scipy.constants import lambda2nu, speed_of_light
+    >>> import numpy as np
+    >>> lambda2nu(np.array((1, speed_of_light)))
+    array([  2.99792458e+08,   1.00000000e+00])
+
+    """
+    xp = array_namespace(lambda_)
+    return c / _asarray(lambda_, xp=xp, subok=True)
+
+
+def nu2lambda(nu: npt.ArrayLike) -> Any:
+    """
+    Convert optical frequency to wavelength.
+
+    Parameters
+    ----------
+    nu : array_like
+        Optical frequency to be converted.
+
+    Returns
+    -------
+    lambda : float or array of floats
+        Equivalent wavelength(s).
+
+    Notes
+    -----
+    Computes ``lambda = c / nu`` where c = 299792458.0, i.e., the
+    (vacuum) speed of light in meters/second.
+
+    Examples
+    --------
+    >>> from scipy.constants import nu2lambda, speed_of_light
+    >>> import numpy as np
+    >>> nu2lambda(np.array((1, speed_of_light)))
+    array([  2.99792458e+08,   1.00000000e+00])
+
+    """
+    xp = array_namespace(nu)
+    return c / _asarray(nu, xp=xp, subok=True)

parrot/lib/python3.10/site-packages/scipy/constants/codata.py

@@ -0,0 +1,21 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.constants` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+__all__ = [  # noqa: F822
+    'physical_constants', 'value', 'unit', 'precision', 'find',
+    'ConstantWarning', 'k', 'c',
+
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package="constants", module="codata",
+                                   private_modules=["_codata"], all=__all__,
+                                   attribute=name)

parrot/lib/python3.10/site-packages/scipy/constants/constants.py

@@ -0,0 +1,53 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.constants` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+
+__all__ = [  # noqa: F822
+    'Avogadro', 'Boltzmann', 'Btu', 'Btu_IT', 'Btu_th', 'G',
+    'Julian_year', 'N_A', 'Planck', 'R', 'Rydberg',
+    'Stefan_Boltzmann', 'Wien', 'acre', 'alpha',
+    'angstrom', 'arcmin', 'arcminute', 'arcsec',
+    'arcsecond', 'astronomical_unit', 'atm',
+    'atmosphere', 'atomic_mass', 'atto', 'au', 'bar',
+    'barrel', 'bbl', 'blob', 'c', 'calorie',
+    'calorie_IT', 'calorie_th', 'carat', 'centi',
+    'convert_temperature', 'day', 'deci', 'degree',
+    'degree_Fahrenheit', 'deka', 'dyn', 'dyne', 'e',
+    'eV', 'electron_mass', 'electron_volt',
+    'elementary_charge', 'epsilon_0', 'erg',
+    'exa', 'exbi', 'femto', 'fermi', 'fine_structure',
+    'fluid_ounce', 'fluid_ounce_US', 'fluid_ounce_imp',
+    'foot', 'g', 'gallon', 'gallon_US', 'gallon_imp',
+    'gas_constant', 'gibi', 'giga', 'golden', 'golden_ratio',
+    'grain', 'gram', 'gravitational_constant', 'h', 'hbar',
+    'hectare', 'hecto', 'horsepower', 'hour', 'hp',
+    'inch', 'k', 'kgf', 'kibi', 'kilo', 'kilogram_force',
+    'kmh', 'knot', 'lambda2nu', 'lb', 'lbf',
+    'light_year', 'liter', 'litre', 'long_ton', 'm_e',
+    'm_n', 'm_p', 'm_u', 'mach', 'mebi', 'mega',
+    'metric_ton', 'micro', 'micron', 'mil', 'mile',
+    'milli', 'minute', 'mmHg', 'mph', 'mu_0', 'nano',
+    'nautical_mile', 'neutron_mass', 'nu2lambda',
+    'ounce', 'oz', 'parsec', 'pebi', 'peta',
+    'pi', 'pico', 'point', 'pound', 'pound_force',
+    'proton_mass', 'psi', 'pt', 'short_ton',
+    'sigma', 'slinch', 'slug', 'speed_of_light',
+    'speed_of_sound', 'stone', 'survey_foot',
+    'survey_mile', 'tebi', 'tera', 'ton_TNT',
+    'torr', 'troy_ounce', 'troy_pound', 'u',
+    'week', 'yard', 'year', 'yobi', 'yocto',
+    'yotta', 'zebi', 'zepto', 'zero_Celsius', 'zetta'
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package="constants", module="constants",
+                                   private_modules=["_constants"], all=__all__,
+                                   attribute=name)

parrot/lib/python3.10/site-packages/scipy/constants/tests/test_codata.py

@@ -0,0 +1,57 @@
+from scipy.constants import find, value, ConstantWarning, c, speed_of_light
+from numpy.testing import (assert_equal, assert_, assert_almost_equal,
+                           suppress_warnings)
+import scipy.constants._codata as _cd
+
+
+def test_find():
+    keys = find('weak mixing', disp=False)
+    assert_equal(keys, ['weak mixing angle'])
+
+    keys = find('qwertyuiop', disp=False)
+    assert_equal(keys, [])
+
+    keys = find('natural unit', disp=False)
+    assert_equal(keys, sorted(['natural unit of velocity',
+                                'natural unit of action',
+                                'natural unit of action in eV s',
+                                'natural unit of mass',
+                                'natural unit of energy',
+                                'natural unit of energy in MeV',
+                                'natural unit of momentum',
+                                'natural unit of momentum in MeV/c',
+                                'natural unit of length',
+                                'natural unit of time']))
+
+
+def test_basic_table_parse():
+    c_s = 'speed of light in vacuum'
+    assert_equal(value(c_s), c)
+    assert_equal(value(c_s), speed_of_light)
+
+
+def test_basic_lookup():
+    assert_equal('%d %s' % (_cd.c, _cd.unit('speed of light in vacuum')),
+                 '299792458 m s^-1')
+
+
+def test_find_all():
+    assert_(len(find(disp=False)) > 300)
+
+
+def test_find_single():
+    assert_equal(find('Wien freq', disp=False)[0],
+                 'Wien frequency displacement law constant')
+
+
+def test_2002_vs_2006():
+    assert_almost_equal(value('magn. flux quantum'),
+                        value('mag. flux quantum'))
+
+
+def test_exact_values():
+    # Check that updating stored values with exact ones worked.
+    with suppress_warnings() as sup:
+        sup.filter(ConstantWarning)
+        for key in _cd.exact_values:
+            assert_((_cd.exact_values[key][0] - value(key)) / value(key) == 0)

parrot/lib/python3.10/site-packages/scipy/constants/tests/test_constants.py

@@ -0,0 +1,89 @@
+import pytest
+
+import scipy.constants as sc
+from scipy.conftest import array_api_compatible
+from scipy._lib._array_api import xp_assert_equal, xp_assert_close
+
+
+pytestmark = [array_api_compatible, pytest.mark.usefixtures("skip_xp_backends")]
+skip_xp_backends = pytest.mark.skip_xp_backends
+
+
+class TestConvertTemperature:
+    def test_convert_temperature(self, xp):
+        xp_assert_equal(sc.convert_temperature(xp.asarray(32.), 'f', 'Celsius'),
+                        xp.asarray(0.0))
+        xp_assert_equal(sc.convert_temperature(xp.asarray([0., 0.]),
+                                               'celsius', 'Kelvin'),
+                        xp.asarray([273.15, 273.15]))
+        xp_assert_equal(sc.convert_temperature(xp.asarray([0., 0.]), 'kelvin', 'c'),
+                        xp.asarray([-273.15, -273.15]))
+        xp_assert_equal(sc.convert_temperature(xp.asarray([32., 32.]), 'f', 'k'),
+                        xp.asarray([273.15, 273.15]))
+        xp_assert_equal(sc.convert_temperature(xp.asarray([273.15, 273.15]),
+                                               'kelvin', 'F'),
+                        xp.asarray([32., 32.]))
+        xp_assert_equal(sc.convert_temperature(xp.asarray([0., 0.]), 'C', 'fahrenheit'),
+                        xp.asarray([32., 32.]))
+        xp_assert_close(sc.convert_temperature(xp.asarray([0., 0.], dtype=xp.float64),
+                                               'c', 'r'),
+                        xp.asarray([491.67, 491.67], dtype=xp.float64),
+                        rtol=0., atol=1e-13)
+        xp_assert_close(sc.convert_temperature(xp.asarray([491.67, 491.67],
+                                                        dtype=xp.float64),
+                                               'Rankine', 'C'),
+                        xp.asarray([0., 0.], dtype=xp.float64), rtol=0., atol=1e-13)
+        xp_assert_close(sc.convert_temperature(xp.asarray([491.67, 491.67],
+                                                        dtype=xp.float64),
+                                               'r', 'F'),
+                        xp.asarray([32., 32.], dtype=xp.float64), rtol=0., atol=1e-13)
+        xp_assert_close(sc.convert_temperature(xp.asarray([32., 32.], dtype=xp.float64),
+                                               'fahrenheit', 'R'),
+                        xp.asarray([491.67, 491.67], dtype=xp.float64),
+                        rtol=0., atol=1e-13)
+        xp_assert_close(sc.convert_temperature(xp.asarray([273.15, 273.15],
+                                                        dtype=xp.float64),
+                                               'K', 'R'),
+                        xp.asarray([491.67, 491.67], dtype=xp.float64),
+                        rtol=0., atol=1e-13)
+        xp_assert_close(sc.convert_temperature(xp.asarray([491.67, 0.],
+                                                          dtype=xp.float64),
+                                               'rankine', 'kelvin'),
+                        xp.asarray([273.15, 0.], dtype=xp.float64), rtol=0., atol=1e-13)
+
+    @skip_xp_backends(np_only=True, reasons=['Python list input uses NumPy backend'])
+    def test_convert_temperature_array_like(self):
+        xp_assert_close(sc.convert_temperature([491.67, 0.], 'rankine', 'kelvin'),
+                        [273.15, 0.], rtol=0., atol=1e-13)
+
+
+    @skip_xp_backends(np_only=True, reasons=['Python int input uses NumPy backend'])
+    def test_convert_temperature_errors(self, xp):
+        with pytest.raises(NotImplementedError, match="old_scale="):
+            sc.convert_temperature(1, old_scale="cheddar", new_scale="kelvin")
+        with pytest.raises(NotImplementedError, match="new_scale="):
+            sc.convert_temperature(1, old_scale="kelvin", new_scale="brie")
+
+
+class TestLambdaToNu:
+    def test_lambda_to_nu(self, xp):
+        xp_assert_equal(sc.lambda2nu(xp.asarray([sc.speed_of_light, 1])),
+                        xp.asarray([1, sc.speed_of_light]))
+
+
+    @skip_xp_backends(np_only=True, reasons=['Python list input uses NumPy backend'])
+    def test_lambda_to_nu_array_like(self, xp):
+        xp_assert_equal(sc.lambda2nu([sc.speed_of_light, 1]),
+                        [1, sc.speed_of_light])
+
+
+class TestNuToLambda:
+    def test_nu_to_lambda(self, xp):
+        xp_assert_equal(sc.nu2lambda(xp.asarray([sc.speed_of_light, 1])),
+                        xp.asarray([1, sc.speed_of_light]))
+
+    @skip_xp_backends(np_only=True, reasons=['Python list input uses NumPy backend'])
+    def test_nu_to_lambda_array_like(self, xp):
+        xp_assert_equal(sc.nu2lambda([sc.speed_of_light, 1]),
+                        [1, sc.speed_of_light])
+

parrot/lib/python3.10/site-packages/scipy/fftpack/_basic.py

@@ -0,0 +1,428 @@
+"""
+Discrete Fourier Transforms - _basic.py
+"""
+# Created by Pearu Peterson, August,September 2002
+__all__ = ['fft','ifft','fftn','ifftn','rfft','irfft',
+           'fft2','ifft2']
+
+from scipy.fft import _pocketfft
+from ._helper import _good_shape
+
+
+def fft(x, n=None, axis=-1, overwrite_x=False):
+    """
+    Return discrete Fourier transform of real or complex sequence.
+
+    The returned complex array contains ``y(0), y(1),..., y(n-1)``, where
+
+    ``y(j) = (x * exp(-2*pi*sqrt(-1)*j*np.arange(n)/n)).sum()``.
+
+    Parameters
+    ----------
+    x : array_like
+        Array to Fourier transform.
+    n : int, optional
+        Length of the Fourier transform. If ``n < x.shape[axis]``, `x` is
+        truncated. If ``n > x.shape[axis]``, `x` is zero-padded. The
+        default results in ``n = x.shape[axis]``.
+    axis : int, optional
+        Axis along which the fft's are computed; the default is over the
+        last axis (i.e., ``axis=-1``).
+    overwrite_x : bool, optional
+        If True, the contents of `x` can be destroyed; the default is False.
+
+    Returns
+    -------
+    z : complex ndarray
+        with the elements::
+
+            [y(0),y(1),..,y(n/2),y(1-n/2),...,y(-1)]        if n is even
+            [y(0),y(1),..,y((n-1)/2),y(-(n-1)/2),...,y(-1)]  if n is odd
+
+        where::
+
+            y(j) = sum[k=0..n-1] x[k] * exp(-sqrt(-1)*j*k* 2*pi/n), j = 0..n-1
+
+    See Also
+    --------
+    ifft : Inverse FFT
+    rfft : FFT of a real sequence
+
+    Notes
+    -----
+    The packing of the result is "standard": If ``A = fft(a, n)``, then
+    ``A[0]`` contains the zero-frequency term, ``A[1:n/2]`` contains the
+    positive-frequency terms, and ``A[n/2:]`` contains the negative-frequency
+    terms, in order of decreasingly negative frequency. So ,for an 8-point
+    transform, the frequencies of the result are [0, 1, 2, 3, -4, -3, -2, -1].
+    To rearrange the fft output so that the zero-frequency component is
+    centered, like [-4, -3, -2, -1,  0,  1,  2,  3], use `fftshift`.
+
+    Both single and double precision routines are implemented. Half precision
+    inputs will be converted to single precision. Non-floating-point inputs
+    will be converted to double precision. Long-double precision inputs are
+    not supported.
+
+    This function is most efficient when `n` is a power of two, and least
+    efficient when `n` is prime.
+
+    Note that if ``x`` is real-valued, then ``A[j] == A[n-j].conjugate()``.
+    If ``x`` is real-valued and ``n`` is even, then ``A[n/2]`` is real.
+
+    If the data type of `x` is real, a "real FFT" algorithm is automatically
+    used, which roughly halves the computation time. To increase efficiency
+    a little further, use `rfft`, which does the same calculation, but only
+    outputs half of the symmetrical spectrum. If the data is both real and
+    symmetrical, the `dct` can again double the efficiency by generating
+    half of the spectrum from half of the signal.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.fftpack import fft, ifft
+    >>> x = np.arange(5)
+    >>> np.allclose(fft(ifft(x)), x, atol=1e-15)  # within numerical accuracy.
+    True
+
+    """
+    return _pocketfft.fft(x, n, axis, None, overwrite_x)
+
+
+def ifft(x, n=None, axis=-1, overwrite_x=False):
+    """
+    Return discrete inverse Fourier transform of real or complex sequence.
+
+    The returned complex array contains ``y(0), y(1),..., y(n-1)``, where
+
+    ``y(j) = (x * exp(2*pi*sqrt(-1)*j*np.arange(n)/n)).mean()``.
+
+    Parameters
+    ----------
+    x : array_like
+        Transformed data to invert.
+    n : int, optional
+        Length of the inverse Fourier transform.  If ``n < x.shape[axis]``,
+        `x` is truncated. If ``n > x.shape[axis]``, `x` is zero-padded.
+        The default results in ``n = x.shape[axis]``.
+    axis : int, optional
+        Axis along which the ifft's are computed; the default is over the
+        last axis (i.e., ``axis=-1``).
+    overwrite_x : bool, optional
+        If True, the contents of `x` can be destroyed; the default is False.
+
+    Returns
+    -------
+    ifft : ndarray of floats
+        The inverse discrete Fourier transform.
+
+    See Also
+    --------
+    fft : Forward FFT
+
+    Notes
+    -----
+    Both single and double precision routines are implemented. Half precision
+    inputs will be converted to single precision. Non-floating-point inputs
+    will be converted to double precision. Long-double precision inputs are
+    not supported.
+
+    This function is most efficient when `n` is a power of two, and least
+    efficient when `n` is prime.
+
+    If the data type of `x` is real, a "real IFFT" algorithm is automatically
+    used, which roughly halves the computation time.
+
+    Examples
+    --------
+    >>> from scipy.fftpack import fft, ifft
+    >>> import numpy as np
+    >>> x = np.arange(5)
+    >>> np.allclose(ifft(fft(x)), x, atol=1e-15)  # within numerical accuracy.
+    True
+
+    """
+    return _pocketfft.ifft(x, n, axis, None, overwrite_x)
+
+
+def rfft(x, n=None, axis=-1, overwrite_x=False):
+    """
+    Discrete Fourier transform of a real sequence.
+
+    Parameters
+    ----------
+    x : array_like, real-valued
+        The data to transform.
+    n : int, optional
+        Defines the length of the Fourier transform. If `n` is not specified
+        (the default) then ``n = x.shape[axis]``. If ``n < x.shape[axis]``,
+        `x` is truncated, if ``n > x.shape[axis]``, `x` is zero-padded.
+    axis : int, optional
+        The axis along which the transform is applied. The default is the
+        last axis.
+    overwrite_x : bool, optional
+        If set to true, the contents of `x` can be overwritten. Default is
+        False.
+
+    Returns
+    -------
+    z : real ndarray
+        The returned real array contains::
+
+          [y(0),Re(y(1)),Im(y(1)),...,Re(y(n/2))]              if n is even
+          [y(0),Re(y(1)),Im(y(1)),...,Re(y(n/2)),Im(y(n/2))]   if n is odd
+
+        where::
+
+          y(j) = sum[k=0..n-1] x[k] * exp(-sqrt(-1)*j*k*2*pi/n)
+          j = 0..n-1
+
+    See Also
+    --------
+    fft, irfft, scipy.fft.rfft
+
+    Notes
+    -----
+    Within numerical accuracy, ``y == rfft(irfft(y))``.
+
+    Both single and double precision routines are implemented. Half precision
+    inputs will be converted to single precision. Non-floating-point inputs
+    will be converted to double precision. Long-double precision inputs are
+    not supported.
+
+    To get an output with a complex datatype, consider using the newer
+    function `scipy.fft.rfft`.
+
+    Examples
+    --------
+    >>> from scipy.fftpack import fft, rfft
+    >>> a = [9, -9, 1, 3]
+    >>> fft(a)
+    array([  4. +0.j,   8.+12.j,  16. +0.j,   8.-12.j])
+    >>> rfft(a)
+    array([  4.,   8.,  12.,  16.])
+
+    """
+    return _pocketfft.rfft_fftpack(x, n, axis, None, overwrite_x)
+
+
+def irfft(x, n=None, axis=-1, overwrite_x=False):
+    """
+    Return inverse discrete Fourier transform of real sequence x.
+
+    The contents of `x` are interpreted as the output of the `rfft`
+    function.
+
+    Parameters
+    ----------
+    x : array_like
+        Transformed data to invert.
+    n : int, optional
+        Length of the inverse Fourier transform.
+        If n < x.shape[axis], x is truncated.
+        If n > x.shape[axis], x is zero-padded.
+        The default results in n = x.shape[axis].
+    axis : int, optional
+        Axis along which the ifft's are computed; the default is over
+        the last axis (i.e., axis=-1).
+    overwrite_x : bool, optional
+        If True, the contents of `x` can be destroyed; the default is False.
+
+    Returns
+    -------
+    irfft : ndarray of floats
+        The inverse discrete Fourier transform.
+
+    See Also
+    --------
+    rfft, ifft, scipy.fft.irfft
+
+    Notes
+    -----
+    The returned real array contains::
+
+        [y(0),y(1),...,y(n-1)]
+
+    where for n is even::
+
+        y(j) = 1/n (sum[k=1..n/2-1] (x[2*k-1]+sqrt(-1)*x[2*k])
+                                     * exp(sqrt(-1)*j*k* 2*pi/n)
+                    + c.c. + x[0] + (-1)**(j) x[n-1])
+
+    and for n is odd::
+
+        y(j) = 1/n (sum[k=1..(n-1)/2] (x[2*k-1]+sqrt(-1)*x[2*k])
+                                     * exp(sqrt(-1)*j*k* 2*pi/n)
+                    + c.c. + x[0])
+
+    c.c. denotes complex conjugate of preceding expression.
+
+    For details on input parameters, see `rfft`.
+
+    To process (conjugate-symmetric) frequency-domain data with a complex
+    datatype, consider using the newer function `scipy.fft.irfft`.
+
+    Examples
+    --------
+    >>> from scipy.fftpack import rfft, irfft
+    >>> a = [1.0, 2.0, 3.0, 4.0, 5.0]
+    >>> irfft(a)
+    array([ 2.6       , -3.16405192,  1.24398433, -1.14955713,  1.46962473])
+    >>> irfft(rfft(a))
+    array([1., 2., 3., 4., 5.])
+
+    """
+    return _pocketfft.irfft_fftpack(x, n, axis, None, overwrite_x)
+
+
+def fftn(x, shape=None, axes=None, overwrite_x=False):
+    """
+    Return multidimensional discrete Fourier transform.
+
+    The returned array contains::
+
+      y[j_1,..,j_d] = sum[k_1=0..n_1-1, ..., k_d=0..n_d-1]
+         x[k_1,..,k_d] * prod[i=1..d] exp(-sqrt(-1)*2*pi/n_i * j_i * k_i)
+
+    where d = len(x.shape) and n = x.shape.
+
+    Parameters
+    ----------
+    x : array_like
+        The (N-D) array to transform.
+    shape : int or array_like of ints or None, optional
+        The shape of the result. If both `shape` and `axes` (see below) are
+        None, `shape` is ``x.shape``; if `shape` is None but `axes` is
+        not None, then `shape` is ``numpy.take(x.shape, axes, axis=0)``.
+        If ``shape[i] > x.shape[i]``, the ith dimension is padded with zeros.
+        If ``shape[i] < x.shape[i]``, the ith dimension is truncated to
+        length ``shape[i]``.
+        If any element of `shape` is -1, the size of the corresponding
+        dimension of `x` is used.
+    axes : int or array_like of ints or None, optional
+        The axes of `x` (`y` if `shape` is not None) along which the
+        transform is applied.
+        The default is over all axes.
+    overwrite_x : bool, optional
+        If True, the contents of `x` can be destroyed. Default is False.
+
+    Returns
+    -------
+    y : complex-valued N-D NumPy array
+        The (N-D) DFT of the input array.
+
+    See Also
+    --------
+    ifftn
+
+    Notes
+    -----
+    If ``x`` is real-valued, then
+    ``y[..., j_i, ...] == y[..., n_i-j_i, ...].conjugate()``.
+
+    Both single and double precision routines are implemented. Half precision
+    inputs will be converted to single precision. Non-floating-point inputs
+    will be converted to double precision. Long-double precision inputs are
+    not supported.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.fftpack import fftn, ifftn
+    >>> y = (-np.arange(16), 8 - np.arange(16), np.arange(16))
+    >>> np.allclose(y, fftn(ifftn(y)))
+    True
+
+    """
+    shape = _good_shape(x, shape, axes)
+    return _pocketfft.fftn(x, shape, axes, None, overwrite_x)
+
+
+def ifftn(x, shape=None, axes=None, overwrite_x=False):
+    """
+    Return inverse multidimensional discrete Fourier transform.
+
+    The sequence can be of an arbitrary type.
+
+    The returned array contains::
+
+      y[j_1,..,j_d] = 1/p * sum[k_1=0..n_1-1, ..., k_d=0..n_d-1]
+         x[k_1,..,k_d] * prod[i=1..d] exp(sqrt(-1)*2*pi/n_i * j_i * k_i)
+
+    where ``d = len(x.shape)``, ``n = x.shape``, and ``p = prod[i=1..d] n_i``.
+
+    For description of parameters see `fftn`.
+
+    See Also
+    --------
+    fftn : for detailed information.
+
+    Examples
+    --------
+    >>> from scipy.fftpack import fftn, ifftn
+    >>> import numpy as np
+    >>> y = (-np.arange(16), 8 - np.arange(16), np.arange(16))
+    >>> np.allclose(y, ifftn(fftn(y)))
+    True
+
+    """
+    shape = _good_shape(x, shape, axes)
+    return _pocketfft.ifftn(x, shape, axes, None, overwrite_x)
+
+
+def fft2(x, shape=None, axes=(-2,-1), overwrite_x=False):
+    """
+    2-D discrete Fourier transform.
+
+    Return the 2-D discrete Fourier transform of the 2-D argument
+    `x`.
+
+    See Also
+    --------
+    fftn : for detailed information.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.fftpack import fft2, ifft2
+    >>> y = np.mgrid[:5, :5][0]
+    >>> y
+    array([[0, 0, 0, 0, 0],
+           [1, 1, 1, 1, 1],
+           [2, 2, 2, 2, 2],
+           [3, 3, 3, 3, 3],
+           [4, 4, 4, 4, 4]])
+    >>> np.allclose(y, ifft2(fft2(y)))
+    True
+    """
+    return fftn(x,shape,axes,overwrite_x)
+
+
+def ifft2(x, shape=None, axes=(-2,-1), overwrite_x=False):
+    """
+    2-D discrete inverse Fourier transform of real or complex sequence.
+
+    Return inverse 2-D discrete Fourier transform of
+    arbitrary type sequence x.
+
+    See `ifft` for more information.
+
+    See Also
+    --------
+    fft2, ifft
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.fftpack import fft2, ifft2
+    >>> y = np.mgrid[:5, :5][0]
+    >>> y
+    array([[0, 0, 0, 0, 0],
+           [1, 1, 1, 1, 1],
+           [2, 2, 2, 2, 2],
+           [3, 3, 3, 3, 3],
+           [4, 4, 4, 4, 4]])
+    >>> np.allclose(y, fft2(ifft2(y)))
+    True
+
+    """
+    return ifftn(x,shape,axes,overwrite_x)

parrot/lib/python3.10/site-packages/scipy/fftpack/_helper.py

@@ -0,0 +1,115 @@
+import operator
+
+import numpy as np
+from numpy.fft import fftshift, ifftshift, fftfreq
+
+import scipy.fft._pocketfft.helper as _helper
+
+__all__ = ['fftshift', 'ifftshift', 'fftfreq', 'rfftfreq', 'next_fast_len']
+
+
+def rfftfreq(n, d=1.0):
+    """DFT sample frequencies (for usage with rfft, irfft).
+
+    The returned float array contains the frequency bins in
+    cycles/unit (with zero at the start) given a window length `n` and a
+    sample spacing `d`::
+
+      f = [0,1,1,2,2,...,n/2-1,n/2-1,n/2]/(d*n)   if n is even
+      f = [0,1,1,2,2,...,n/2-1,n/2-1,n/2,n/2]/(d*n)   if n is odd
+
+    Parameters
+    ----------
+    n : int
+        Window length.
+    d : scalar, optional
+        Sample spacing. Default is 1.
+
+    Returns
+    -------
+    out : ndarray
+        The array of length `n`, containing the sample frequencies.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import fftpack
+    >>> sig = np.array([-2, 8, 6, 4, 1, 0, 3, 5], dtype=float)
+    >>> sig_fft = fftpack.rfft(sig)
+    >>> n = sig_fft.size
+    >>> timestep = 0.1
+    >>> freq = fftpack.rfftfreq(n, d=timestep)
+    >>> freq
+    array([ 0.  ,  1.25,  1.25,  2.5 ,  2.5 ,  3.75,  3.75,  5.  ])
+
+    """
+    n = operator.index(n)
+    if n < 0:
+        raise ValueError("n = %s is not valid. "
+                         "n must be a nonnegative integer." % n)
+
+    return (np.arange(1, n + 1, dtype=int) // 2) / float(n * d)
+
+
+def next_fast_len(target):
+    """
+    Find the next fast size of input data to `fft`, for zero-padding, etc.
+
+    SciPy's FFTPACK has efficient functions for radix {2, 3, 4, 5}, so this
+    returns the next composite of the prime factors 2, 3, and 5 which is
+    greater than or equal to `target`. (These are also known as 5-smooth
+    numbers, regular numbers, or Hamming numbers.)
+
+    Parameters
+    ----------
+    target : int
+        Length to start searching from. Must be a positive integer.
+
+    Returns
+    -------
+    out : int
+        The first 5-smooth number greater than or equal to `target`.
+
+    Notes
+    -----
+    .. versionadded:: 0.18.0
+
+    Examples
+    --------
+    On a particular machine, an FFT of prime length takes 133 ms:
+
+    >>> from scipy import fftpack
+    >>> import numpy as np
+    >>> rng = np.random.default_rng()
+    >>> min_len = 10007  # prime length is worst case for speed
+    >>> a = rng.standard_normal(min_len)
+    >>> b = fftpack.fft(a)
+
+    Zero-padding to the next 5-smooth length reduces computation time to
+    211 us, a speedup of 630 times:
+
+    >>> fftpack.next_fast_len(min_len)
+    10125
+    >>> b = fftpack.fft(a, 10125)
+
+    Rounding up to the next power of 2 is not optimal, taking 367 us to
+    compute, 1.7 times as long as the 5-smooth size:
+
+    >>> b = fftpack.fft(a, 16384)
+
+    """
+    # Real transforms use regular sizes so this is backwards compatible
+    return _helper.good_size(target, True)
+
+
+def _good_shape(x, shape, axes):
+    """Ensure that shape argument is valid for scipy.fftpack
+
+    scipy.fftpack does not support len(shape) < x.ndim when axes is not given.
+    """
+    if shape is not None and axes is None:
+        shape = _helper._iterable_of_int(shape, 'shape')
+        if len(shape) != np.ndim(x):
+            raise ValueError("when given, axes and shape arguments"
+                             " have to be of the same length")
+    return shape

parrot/lib/python3.10/site-packages/scipy/fftpack/_realtransforms.py

@@ -0,0 +1,598 @@
+"""
+Real spectrum transforms (DCT, DST, MDCT)
+"""
+
+__all__ = ['dct', 'idct', 'dst', 'idst', 'dctn', 'idctn', 'dstn', 'idstn']
+
+from scipy.fft import _pocketfft
+from ._helper import _good_shape
+
+_inverse_typemap = {1: 1, 2: 3, 3: 2, 4: 4}
+
+
+def dctn(x, type=2, shape=None, axes=None, norm=None, overwrite_x=False):
+    """
+    Return multidimensional Discrete Cosine Transform along the specified axes.
+
+    Parameters
+    ----------
+    x : array_like
+        The input array.
+    type : {1, 2, 3, 4}, optional
+        Type of the DCT (see Notes). Default type is 2.
+    shape : int or array_like of ints or None, optional
+        The shape of the result. If both `shape` and `axes` (see below) are
+        None, `shape` is ``x.shape``; if `shape` is None but `axes` is
+        not None, then `shape` is ``numpy.take(x.shape, axes, axis=0)``.
+        If ``shape[i] > x.shape[i]``, the ith dimension is padded with zeros.
+        If ``shape[i] < x.shape[i]``, the ith dimension is truncated to
+        length ``shape[i]``.
+        If any element of `shape` is -1, the size of the corresponding
+        dimension of `x` is used.
+    axes : int or array_like of ints or None, optional
+        Axes along which the DCT is computed.
+        The default is over all axes.
+    norm : {None, 'ortho'}, optional
+        Normalization mode (see Notes). Default is None.
+    overwrite_x : bool, optional
+        If True, the contents of `x` can be destroyed; the default is False.
+
+    Returns
+    -------
+    y : ndarray of real
+        The transformed input array.
+
+    See Also
+    --------
+    idctn : Inverse multidimensional DCT
+
+    Notes
+    -----
+    For full details of the DCT types and normalization modes, as well as
+    references, see `dct`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.fftpack import dctn, idctn
+    >>> rng = np.random.default_rng()
+    >>> y = rng.standard_normal((16, 16))
+    >>> np.allclose(y, idctn(dctn(y, norm='ortho'), norm='ortho'))
+    True
+
+    """
+    shape = _good_shape(x, shape, axes)
+    return _pocketfft.dctn(x, type, shape, axes, norm, overwrite_x)
+
+
+def idctn(x, type=2, shape=None, axes=None, norm=None, overwrite_x=False):
+    """
+    Return multidimensional Discrete Cosine Transform along the specified axes.
+
+    Parameters
+    ----------
+    x : array_like
+        The input array.
+    type : {1, 2, 3, 4}, optional
+        Type of the DCT (see Notes). Default type is 2.
+    shape : int or array_like of ints or None, optional
+        The shape of the result.  If both `shape` and `axes` (see below) are
+        None, `shape` is ``x.shape``; if `shape` is None but `axes` is
+        not None, then `shape` is ``numpy.take(x.shape, axes, axis=0)``.
+        If ``shape[i] > x.shape[i]``, the ith dimension is padded with zeros.
+        If ``shape[i] < x.shape[i]``, the ith dimension is truncated to
+        length ``shape[i]``.
+        If any element of `shape` is -1, the size of the corresponding
+        dimension of `x` is used.
+    axes : int or array_like of ints or None, optional
+        Axes along which the IDCT is computed.
+        The default is over all axes.
+    norm : {None, 'ortho'}, optional
+        Normalization mode (see Notes). Default is None.
+    overwrite_x : bool, optional
+        If True, the contents of `x` can be destroyed; the default is False.
+
+    Returns
+    -------
+    y : ndarray of real
+        The transformed input array.
+
+    See Also
+    --------
+    dctn : multidimensional DCT
+
+    Notes
+    -----
+    For full details of the IDCT types and normalization modes, as well as
+    references, see `idct`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.fftpack import dctn, idctn
+    >>> rng = np.random.default_rng()
+    >>> y = rng.standard_normal((16, 16))
+    >>> np.allclose(y, idctn(dctn(y, norm='ortho'), norm='ortho'))
+    True
+
+    """
+    type = _inverse_typemap[type]
+    shape = _good_shape(x, shape, axes)
+    return _pocketfft.dctn(x, type, shape, axes, norm, overwrite_x)
+
+
+def dstn(x, type=2, shape=None, axes=None, norm=None, overwrite_x=False):
+    """
+    Return multidimensional Discrete Sine Transform along the specified axes.
+
+    Parameters
+    ----------
+    x : array_like
+        The input array.
+    type : {1, 2, 3, 4}, optional
+        Type of the DST (see Notes). Default type is 2.
+    shape : int or array_like of ints or None, optional
+        The shape of the result.  If both `shape` and `axes` (see below) are
+        None, `shape` is ``x.shape``; if `shape` is None but `axes` is
+        not None, then `shape` is ``numpy.take(x.shape, axes, axis=0)``.
+        If ``shape[i] > x.shape[i]``, the ith dimension is padded with zeros.
+        If ``shape[i] < x.shape[i]``, the ith dimension is truncated to
+        length ``shape[i]``.
+        If any element of `shape` is -1, the size of the corresponding
+        dimension of `x` is used.
+    axes : int or array_like of ints or None, optional
+        Axes along which the DCT is computed.
+        The default is over all axes.
+    norm : {None, 'ortho'}, optional
+        Normalization mode (see Notes). Default is None.
+    overwrite_x : bool, optional
+        If True, the contents of `x` can be destroyed; the default is False.
+
+    Returns
+    -------
+    y : ndarray of real
+        The transformed input array.
+
+    See Also
+    --------
+    idstn : Inverse multidimensional DST
+
+    Notes
+    -----
+    For full details of the DST types and normalization modes, as well as
+    references, see `dst`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.fftpack import dstn, idstn
+    >>> rng = np.random.default_rng()
+    >>> y = rng.standard_normal((16, 16))
+    >>> np.allclose(y, idstn(dstn(y, norm='ortho'), norm='ortho'))
+    True
+
+    """
+    shape = _good_shape(x, shape, axes)
+    return _pocketfft.dstn(x, type, shape, axes, norm, overwrite_x)
+
+
+def idstn(x, type=2, shape=None, axes=None, norm=None, overwrite_x=False):
+    """
+    Return multidimensional Discrete Sine Transform along the specified axes.
+
+    Parameters
+    ----------
+    x : array_like
+        The input array.
+    type : {1, 2, 3, 4}, optional
+        Type of the DST (see Notes). Default type is 2.
+    shape : int or array_like of ints or None, optional
+        The shape of the result.  If both `shape` and `axes` (see below) are
+        None, `shape` is ``x.shape``; if `shape` is None but `axes` is
+        not None, then `shape` is ``numpy.take(x.shape, axes, axis=0)``.
+        If ``shape[i] > x.shape[i]``, the ith dimension is padded with zeros.
+        If ``shape[i] < x.shape[i]``, the ith dimension is truncated to
+        length ``shape[i]``.
+        If any element of `shape` is -1, the size of the corresponding
+        dimension of `x` is used.
+    axes : int or array_like of ints or None, optional
+        Axes along which the IDST is computed.
+        The default is over all axes.
+    norm : {None, 'ortho'}, optional
+        Normalization mode (see Notes). Default is None.
+    overwrite_x : bool, optional
+        If True, the contents of `x` can be destroyed; the default is False.
+
+    Returns
+    -------
+    y : ndarray of real
+        The transformed input array.
+
+    See Also
+    --------
+    dstn : multidimensional DST
+
+    Notes
+    -----
+    For full details of the IDST types and normalization modes, as well as
+    references, see `idst`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.fftpack import dstn, idstn
+    >>> rng = np.random.default_rng()
+    >>> y = rng.standard_normal((16, 16))
+    >>> np.allclose(y, idstn(dstn(y, norm='ortho'), norm='ortho'))
+    True
+
+    """
+    type = _inverse_typemap[type]
+    shape = _good_shape(x, shape, axes)
+    return _pocketfft.dstn(x, type, shape, axes, norm, overwrite_x)
+
+
+def dct(x, type=2, n=None, axis=-1, norm=None, overwrite_x=False):
+    r"""
+    Return the Discrete Cosine Transform of arbitrary type sequence x.
+
+    Parameters
+    ----------
+    x : array_like
+        The input array.
+    type : {1, 2, 3, 4}, optional
+        Type of the DCT (see Notes). Default type is 2.
+    n : int, optional
+        Length of the transform.  If ``n < x.shape[axis]``, `x` is
+        truncated.  If ``n > x.shape[axis]``, `x` is zero-padded. The
+        default results in ``n = x.shape[axis]``.
+    axis : int, optional
+        Axis along which the dct is computed; the default is over the
+        last axis (i.e., ``axis=-1``).
+    norm : {None, 'ortho'}, optional
+        Normalization mode (see Notes). Default is None.
+    overwrite_x : bool, optional
+        If True, the contents of `x` can be destroyed; the default is False.
+
+    Returns
+    -------
+    y : ndarray of real
+        The transformed input array.
+
+    See Also
+    --------
+    idct : Inverse DCT
+
+    Notes
+    -----
+    For a single dimension array ``x``, ``dct(x, norm='ortho')`` is equal to
+    MATLAB ``dct(x)``.
+
+    There are, theoretically, 8 types of the DCT, only the first 4 types are
+    implemented in scipy. 'The' DCT generally refers to DCT type 2, and 'the'
+    Inverse DCT generally refers to DCT type 3.
+
+    **Type I**
+
+    There are several definitions of the DCT-I; we use the following
+    (for ``norm=None``)
+
+    .. math::
+
+       y_k = x_0 + (-1)^k x_{N-1} + 2 \sum_{n=1}^{N-2} x_n \cos\left(
+       \frac{\pi k n}{N-1} \right)
+
+    If ``norm='ortho'``, ``x[0]`` and ``x[N-1]`` are multiplied by a scaling
+    factor of :math:`\sqrt{2}`, and ``y[k]`` is multiplied by a scaling factor
+    ``f``
+
+    .. math::
+
+        f = \begin{cases}
+         \frac{1}{2}\sqrt{\frac{1}{N-1}} & \text{if }k=0\text{ or }N-1, \\
+         \frac{1}{2}\sqrt{\frac{2}{N-1}} & \text{otherwise} \end{cases}
+
+    .. versionadded:: 1.2.0
+       Orthonormalization in DCT-I.
+
+    .. note::
+       The DCT-I is only supported for input size > 1.
+
+    **Type II**
+
+    There are several definitions of the DCT-II; we use the following
+    (for ``norm=None``)
+
+    .. math::
+
+       y_k = 2 \sum_{n=0}^{N-1} x_n \cos\left(\frac{\pi k(2n+1)}{2N} \right)
+
+    If ``norm='ortho'``, ``y[k]`` is multiplied by a scaling factor ``f``
+
+    .. math::
+       f = \begin{cases}
+       \sqrt{\frac{1}{4N}} & \text{if }k=0, \\
+       \sqrt{\frac{1}{2N}} & \text{otherwise} \end{cases}
+
+    which makes the corresponding matrix of coefficients orthonormal
+    (``O @ O.T = np.eye(N)``).
+
+    **Type III**
+
+    There are several definitions, we use the following (for ``norm=None``)
+
+    .. math::
+
+       y_k = x_0 + 2 \sum_{n=1}^{N-1} x_n \cos\left(\frac{\pi(2k+1)n}{2N}\right)
+
+    or, for ``norm='ortho'``
+
+    .. math::
+
+       y_k = \frac{x_0}{\sqrt{N}} + \sqrt{\frac{2}{N}} \sum_{n=1}^{N-1} x_n
+       \cos\left(\frac{\pi(2k+1)n}{2N}\right)
+
+    The (unnormalized) DCT-III is the inverse of the (unnormalized) DCT-II, up
+    to a factor `2N`. The orthonormalized DCT-III is exactly the inverse of
+    the orthonormalized DCT-II.
+
+    **Type IV**
+
+    There are several definitions of the DCT-IV; we use the following
+    (for ``norm=None``)
+
+    .. math::
+
+       y_k = 2 \sum_{n=0}^{N-1} x_n \cos\left(\frac{\pi(2k+1)(2n+1)}{4N} \right)
+
+    If ``norm='ortho'``, ``y[k]`` is multiplied by a scaling factor ``f``
+
+    .. math::
+
+        f = \frac{1}{\sqrt{2N}}
+
+    .. versionadded:: 1.2.0
+       Support for DCT-IV.
+
+    References
+    ----------
+    .. [1] 'A Fast Cosine Transform in One and Two Dimensions', by J.
+           Makhoul, `IEEE Transactions on acoustics, speech and signal
+           processing` vol. 28(1), pp. 27-34,
+           :doi:`10.1109/TASSP.1980.1163351` (1980).
+    .. [2] Wikipedia, "Discrete cosine transform",
+           https://en.wikipedia.org/wiki/Discrete_cosine_transform
+
+    Examples
+    --------
+    The Type 1 DCT is equivalent to the FFT (though faster) for real,
+    even-symmetrical inputs. The output is also real and even-symmetrical.
+    Half of the FFT input is used to generate half of the FFT output:
+
+    >>> from scipy.fftpack import fft, dct
+    >>> import numpy as np
+    >>> fft(np.array([4., 3., 5., 10., 5., 3.])).real
+    array([ 30.,  -8.,   6.,  -2.,   6.,  -8.])
+    >>> dct(np.array([4., 3., 5., 10.]), 1)
+    array([ 30.,  -8.,   6.,  -2.])
+
+    """
+    return _pocketfft.dct(x, type, n, axis, norm, overwrite_x)
+
+
+def idct(x, type=2, n=None, axis=-1, norm=None, overwrite_x=False):
+    """
+    Return the Inverse Discrete Cosine Transform of an arbitrary type sequence.
+
+    Parameters
+    ----------
+    x : array_like
+        The input array.
+    type : {1, 2, 3, 4}, optional
+        Type of the DCT (see Notes). Default type is 2.
+    n : int, optional
+        Length of the transform.  If ``n < x.shape[axis]``, `x` is
+        truncated.  If ``n > x.shape[axis]``, `x` is zero-padded. The
+        default results in ``n = x.shape[axis]``.
+    axis : int, optional
+        Axis along which the idct is computed; the default is over the
+        last axis (i.e., ``axis=-1``).
+    norm : {None, 'ortho'}, optional
+        Normalization mode (see Notes). Default is None.
+    overwrite_x : bool, optional
+        If True, the contents of `x` can be destroyed; the default is False.
+
+    Returns
+    -------
+    idct : ndarray of real
+        The transformed input array.
+
+    See Also
+    --------
+    dct : Forward DCT
+
+    Notes
+    -----
+    For a single dimension array `x`, ``idct(x, norm='ortho')`` is equal to
+    MATLAB ``idct(x)``.
+
+    'The' IDCT is the IDCT of type 2, which is the same as DCT of type 3.
+
+    IDCT of type 1 is the DCT of type 1, IDCT of type 2 is the DCT of type
+    3, and IDCT of type 3 is the DCT of type 2. IDCT of type 4 is the DCT
+    of type 4. For the definition of these types, see `dct`.
+
+    Examples
+    --------
+    The Type 1 DCT is equivalent to the DFT for real, even-symmetrical
+    inputs. The output is also real and even-symmetrical. Half of the IFFT
+    input is used to generate half of the IFFT output:
+
+    >>> from scipy.fftpack import ifft, idct
+    >>> import numpy as np
+    >>> ifft(np.array([ 30.,  -8.,   6.,  -2.,   6.,  -8.])).real
+    array([  4.,   3.,   5.,  10.,   5.,   3.])
+    >>> idct(np.array([ 30.,  -8.,   6.,  -2.]), 1) / 6
+    array([  4.,   3.,   5.,  10.])
+
+    """
+    type = _inverse_typemap[type]
+    return _pocketfft.dct(x, type, n, axis, norm, overwrite_x)
+
+
+def dst(x, type=2, n=None, axis=-1, norm=None, overwrite_x=False):
+    r"""
+    Return the Discrete Sine Transform of arbitrary type sequence x.
+
+    Parameters
+    ----------
+    x : array_like
+        The input array.
+    type : {1, 2, 3, 4}, optional
+        Type of the DST (see Notes). Default type is 2.
+    n : int, optional
+        Length of the transform.  If ``n < x.shape[axis]``, `x` is
+        truncated.  If ``n > x.shape[axis]``, `x` is zero-padded. The
+        default results in ``n = x.shape[axis]``.
+    axis : int, optional
+        Axis along which the dst is computed; the default is over the
+        last axis (i.e., ``axis=-1``).
+    norm : {None, 'ortho'}, optional
+        Normalization mode (see Notes). Default is None.
+    overwrite_x : bool, optional
+        If True, the contents of `x` can be destroyed; the default is False.
+
+    Returns
+    -------
+    dst : ndarray of reals
+        The transformed input array.
+
+    See Also
+    --------
+    idst : Inverse DST
+
+    Notes
+    -----
+    For a single dimension array ``x``.
+
+    There are, theoretically, 8 types of the DST for different combinations of
+    even/odd boundary conditions and boundary off sets [1]_, only the first
+    4 types are implemented in scipy.
+
+    **Type I**
+
+    There are several definitions of the DST-I; we use the following
+    for ``norm=None``. DST-I assumes the input is odd around `n=-1` and `n=N`.
+
+    .. math::
+
+        y_k = 2 \sum_{n=0}^{N-1} x_n \sin\left(\frac{\pi(k+1)(n+1)}{N+1}\right)
+
+    Note that the DST-I is only supported for input size > 1.
+    The (unnormalized) DST-I is its own inverse, up to a factor `2(N+1)`.
+    The orthonormalized DST-I is exactly its own inverse.
+
+    **Type II**
+
+    There are several definitions of the DST-II; we use the following for
+    ``norm=None``. DST-II assumes the input is odd around `n=-1/2` and
+    `n=N-1/2`; the output is odd around :math:`k=-1` and even around `k=N-1`
+
+    .. math::
+
+        y_k = 2 \sum_{n=0}^{N-1} x_n \sin\left(\frac{\pi(k+1)(2n+1)}{2N}\right)
+
+    if ``norm='ortho'``, ``y[k]`` is multiplied by a scaling factor ``f``
+
+    .. math::
+
+        f = \begin{cases}
+        \sqrt{\frac{1}{4N}} & \text{if }k = 0, \\
+        \sqrt{\frac{1}{2N}} & \text{otherwise} \end{cases}
+
+    **Type III**
+
+    There are several definitions of the DST-III, we use the following (for
+    ``norm=None``). DST-III assumes the input is odd around `n=-1` and even
+    around `n=N-1`
+
+    .. math::
+
+        y_k = (-1)^k x_{N-1} + 2 \sum_{n=0}^{N-2} x_n \sin\left(
+        \frac{\pi(2k+1)(n+1)}{2N}\right)
+
+    The (unnormalized) DST-III is the inverse of the (unnormalized) DST-II, up
+    to a factor `2N`. The orthonormalized DST-III is exactly the inverse of the
+    orthonormalized DST-II.
+
+    .. versionadded:: 0.11.0
+
+    **Type IV**
+
+    There are several definitions of the DST-IV, we use the following (for
+    ``norm=None``). DST-IV assumes the input is odd around `n=-0.5` and even
+    around `n=N-0.5`
+
+    .. math::
+
+        y_k = 2 \sum_{n=0}^{N-1} x_n \sin\left(\frac{\pi(2k+1)(2n+1)}{4N}\right)
+
+    The (unnormalized) DST-IV is its own inverse, up to a factor `2N`. The
+    orthonormalized DST-IV is exactly its own inverse.
+
+    .. versionadded:: 1.2.0
+       Support for DST-IV.
+
+    References
+    ----------
+    .. [1] Wikipedia, "Discrete sine transform",
+           https://en.wikipedia.org/wiki/Discrete_sine_transform
+
+    """
+    return _pocketfft.dst(x, type, n, axis, norm, overwrite_x)
+
+
+def idst(x, type=2, n=None, axis=-1, norm=None, overwrite_x=False):
+    """
+    Return the Inverse Discrete Sine Transform of an arbitrary type sequence.
+
+    Parameters
+    ----------
+    x : array_like
+        The input array.
+    type : {1, 2, 3, 4}, optional
+        Type of the DST (see Notes). Default type is 2.
+    n : int, optional
+        Length of the transform.  If ``n < x.shape[axis]``, `x` is
+        truncated. If ``n > x.shape[axis]``, `x` is zero-padded. The
+        default results in ``n = x.shape[axis]``.
+    axis : int, optional
+        Axis along which the idst is computed; the default is over the
+        last axis (i.e., ``axis=-1``).
+    norm : {None, 'ortho'}, optional
+        Normalization mode (see Notes). Default is None.
+    overwrite_x : bool, optional
+        If True, the contents of `x` can be destroyed; the default is False.
+
+    Returns
+    -------
+    idst : ndarray of real
+        The transformed input array.
+
+    See Also
+    --------
+    dst : Forward DST
+
+    Notes
+    -----
+    'The' IDST is the IDST of type 2, which is the same as DST of type 3.
+
+    IDST of type 1 is the DST of type 1, IDST of type 2 is the DST of type
+    3, and IDST of type 3 is the DST of type 2. For the definition of these
+    types, see `dst`.
+
+    .. versionadded:: 0.11.0
+
+    """
+    type = _inverse_typemap[type]
+    return _pocketfft.dst(x, type, n, axis, norm, overwrite_x)

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_to_sparse_semi_structured.h

@@ -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/_to_sparse_semi_structured_ops.h>
+
+namespace at {
+
+
+// aten::_to_sparse_semi_structured(Tensor dense) -> (Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor> _to_sparse_semi_structured(const at::Tensor & dense) {
+    return at::_ops::_to_sparse_semi_structured::call(dense);
+}
+
+}

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/col2im_ops.h

@@ -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 col2im_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, at::IntArrayRef, at::IntArrayRef, at::IntArrayRef, at::IntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::col2im")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out);
+};
+
+struct TORCH_API col2im {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, at::IntArrayRef, at::IntArrayRef, at::IntArrayRef, at::IntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::col2im")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride);
+};
+
+}} // namespace at::_ops

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/logaddexp_native.h

@@ -0,0 +1,23 @@
+#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>
+#include <ATen/ops/logaddexp_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_logaddexp_out : public at::meta::structured_logaddexp {
+void impl(const at::Tensor & self, const at::Tensor & other, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/view_meta_dispatch.h

@@ -0,0 +1,24 @@
+#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 meta {
+
+TORCH_API at::Tensor view(const at::Tensor & self, at::IntArrayRef size);
+TORCH_API at::Tensor view_symint(const at::Tensor & self, c10::SymIntArrayRef size);
+
+} // namespace meta
+} // namespace at