move the text encoder

modeling_clip_encoder.py

@@ -1,682 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The OpenAI Team Authors and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-from dataclasses import dataclass
-from typing import Any, Optional, Tuple, Union
-
-import torch
-from torch import nn
-
-from transformers import CLIPConfig, CLIPModel, CLIPTextConfig, CLIPVisionConfig, GPT2Tokenizer
-from transformers.activations import ACT2FN
-from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (
-    ModelOutput,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "fusing/glide-base"
-
-CLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "fusing/glide-base",
-    # See all CLIP models at https://huggingface.co/models?filter=clip
-]
-
-
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-
-
-# contrastive loss function, adapted from
-# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
-def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
-    return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))
-
-
-def clip_loss(similarity: torch.Tensor) -> torch.Tensor:
-    caption_loss = contrastive_loss(similarity)
-    image_loss = contrastive_loss(similarity.T)
-    return (caption_loss + image_loss) / 2.0
-
-
-@dataclass
-class CLIPOutput(ModelOutput):
-    """
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
-            Contrastive loss for image-text similarity.
-        logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
-            The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
-            similarity scores.
-        logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
-            The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
-            similarity scores.
-        text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
-            The text embeddings obtained by applying the projection layer to the pooled output of [`CLIPTextModel`].
-        image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
-            The image embeddings obtained by applying the projection layer to the pooled output of [`CLIPVisionModel`].
-        text_model_output(`BaseModelOutputWithPooling`):
-            The output of the [`CLIPTextModel`].
-        vision_model_output(`BaseModelOutputWithPooling`):
-            The output of the [`CLIPVisionModel`].
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits_per_image: torch.FloatTensor = None
-    logits_per_text: torch.FloatTensor = None
-    text_embeds: torch.FloatTensor = None
-    image_embeds: torch.FloatTensor = None
-    text_model_output: BaseModelOutputWithPooling = None
-    vision_model_output: BaseModelOutputWithPooling = None
-
-    def to_tuple(self) -> Tuple[Any]:
-        return tuple(
-            self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
-            for k in self.keys()
-        )
-
-
-class CLIPVisionEmbeddings(nn.Module):
-    def __init__(self, config: CLIPVisionConfig):
-        super().__init__()
-        self.config = config
-        self.embed_dim = config.hidden_size
-        self.image_size = config.image_size
-        self.patch_size = config.patch_size
-
-        self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
-
-        self.patch_embedding = nn.Conv2d(
-            in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False
-        )
-
-        self.num_patches = (self.image_size // self.patch_size) ** 2
-        self.num_positions = self.num_patches + 1
-        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
-        self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)))
-
-    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
-        batch_size = pixel_values.shape[0]
-        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, width, grid, grid]
-        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
-
-        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
-        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
-        embeddings = embeddings + self.position_embedding(self.position_ids)
-        return embeddings
-
-
-class CLIPTextEmbeddings(nn.Module):
-    def __init__(self, config: CLIPTextConfig):
-        super().__init__()
-        embed_dim = config.hidden_size
-
-        self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
-        self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
-        self.use_padding_embeddings = config.use_padding_embeddings
-        if self.use_padding_embeddings:
-            self.padding_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, :seq_length]
-
-        if inputs_embeds is None:
-            inputs_embeds = self.token_embedding(input_ids)
-
-        position_embeddings = self.position_embedding(position_ids)
-        embeddings = inputs_embeds + position_embeddings
-
-        if self.use_padding_embeddings and attention_mask is not None:
-            padding_embeddings = self.padding_embedding(position_ids)
-            embeddings = torch.where(attention_mask.bool().unsqueeze(-1), embeddings, padding_embeddings)
-
-        return embeddings
-
-
-class CLIPAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.embed_dim = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.embed_dim // self.num_heads
-        if self.head_dim * self.num_heads != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
-                f" {self.num_heads})."
-            )
-        self.scale = 1 / math.sqrt(math.sqrt(self.head_dim))
-
-        self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3)
-        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        causal_attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        bsz, tgt_len, embed_dim = hidden_states.size()
-
-        qkv_states = self.qkv_proj(hidden_states)
-        qkv_states = qkv_states.view(bsz, tgt_len, self.num_heads, -1)
-        query_states, key_states, value_states = torch.split(qkv_states, self.head_dim, dim=-1)
-
-        attn_weights = torch.einsum("bthc,bshc->bhts", query_states * self.scale, key_states * self.scale)
-
-        wdtype = attn_weights.dtype
-        attn_weights = nn.functional.softmax(attn_weights.float(), dim=-1).type(wdtype)
-
-        attn_output = torch.einsum("bhts,bshc->bthc", attn_weights, value_states)
-        attn_output = attn_output.reshape(bsz, tgt_len, -1)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights
-
-
-class CLIPMLP(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.activation_fn = ACT2FN[config.hidden_act]
-        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
-        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.fc1(hidden_states)
-        hidden_states = self.activation_fn(hidden_states)
-        hidden_states = self.fc2(hidden_states)
-        return hidden_states
-
-
-class CLIPEncoderLayer(nn.Module):
-    def __init__(self, config: CLIPConfig):
-        super().__init__()
-        self.embed_dim = config.hidden_size
-        self.self_attn = CLIPAttention(config)
-        self.layer_norm1 = nn.LayerNorm(self.embed_dim)
-        self.mlp = CLIPMLP(config)
-        self.layer_norm2 = nn.LayerNorm(self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor,
-        causal_attention_mask: torch.Tensor,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-                `(config.encoder_attention_heads,)`.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-
-        hidden_states = self.layer_norm1(hidden_states)
-        hidden_states, attn_weights = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            causal_attention_mask=causal_attention_mask,
-            output_attentions=output_attentions,
-        )
-        hidden_states = residual + hidden_states
-
-        residual = hidden_states
-        hidden_states = self.layer_norm2(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs
-
-
-class CLIPPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = CLIPConfig
-    base_model_prefix = "clip"
-    supports_gradient_checkpointing = True
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        factor = self.config.initializer_factor
-        if isinstance(module, CLIPTextEmbeddings):
-            module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
-            module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
-            if hasattr(module, "padding_embedding"):
-                module.padding_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
-        elif isinstance(module, CLIPVisionEmbeddings):
-            factor = self.config.initializer_factor
-            nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
-            nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
-            nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor)
-        elif isinstance(module, CLIPAttention):
-            factor = self.config.initializer_factor
-            in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
-            out_proj_std = (module.embed_dim**-0.5) * factor
-            nn.init.normal_(module.qkv_proj.weight, std=in_proj_std)
-            nn.init.normal_(module.out_proj.weight, std=out_proj_std)
-        elif isinstance(module, CLIPMLP):
-            factor = self.config.initializer_factor
-            in_proj_std = (
-                (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
-            )
-            fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
-            nn.init.normal_(module.fc1.weight, std=fc_std)
-            nn.init.normal_(module.fc2.weight, std=in_proj_std)
-        elif isinstance(module, CLIPModel):
-            nn.init.normal_(
-                module.text_projection.weight,
-                std=module.text_embed_dim**-0.5 * self.config.initializer_factor,
-            )
-            nn.init.normal_(
-                module.visual_projection.weight,
-                std=module.vision_embed_dim**-0.5 * self.config.initializer_factor,
-            )
-
-        if isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        if isinstance(module, nn.Linear) and module.bias is not None:
-            module.bias.data.zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, CLIPEncoder):
-            module.gradient_checkpointing = value
-
-
-CLIP_START_DOCSTRING = r"""
-    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
-    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
-    behavior.
-
-    Parameters:
-        config ([`CLIPConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-CLIP_TEXT_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-CLIP_VISION_INPUTS_DOCSTRING = r"""
-    Args:
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
-            [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-CLIP_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
-            [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
-        return_loss (`bool`, *optional*):
-            Whether or not to return the contrastive loss.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-class CLIPEncoder(nn.Module):
-    """
-    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
-    [`CLIPEncoderLayer`].
-
-    Args:
-        config: CLIPConfig
-    """
-
-    def __init__(self, config: CLIPConfig):
-        super().__init__()
-        self.config = config
-        self.layers = nn.ModuleList([CLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        inputs_embeds,
-        attention_mask: Optional[torch.Tensor] = None,
-        causal_attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutput]:
-        r"""
-        Args:
-            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
-                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
-                than the model's internal embedding lookup matrix.
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Causal mask for the text model. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        encoder_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
-        hidden_states = inputs_embeds
-        for idx, encoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                encoder_states = encoder_states + (hidden_states,)
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(encoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    causal_attention_mask,
-                )
-            else:
-                layer_outputs = encoder_layer(
-                    hidden_states,
-                    attention_mask,
-                    causal_attention_mask,
-                    output_attentions=output_attentions,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            encoder_states = encoder_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
-        )
-
-
-class CLIPTextTransformer(nn.Module):
-    def __init__(self, config: CLIPTextConfig):
-        super().__init__()
-        self.config = config
-        embed_dim = config.hidden_size
-        self.embeddings = CLIPTextEmbeddings(config)
-        self.encoder = CLIPEncoder(config)
-        self.final_layer_norm = nn.LayerNorm(embed_dim)
-
-    @add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPTextConfig)
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
-        r"""
-        Returns:
-
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if input_ids is None:
-            raise ValueError("You have to specify either input_ids")
-
-        input_shape = input_ids.size()
-        input_ids = input_ids.view(-1, input_shape[-1])
-
-        hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids, attention_mask=attention_mask)
-
-        bsz, seq_len = input_shape
-        # CLIP's text model uses causal mask, prepare it here.
-        # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
-        causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len).to(hidden_states.device)
-
-        # expand attention_mask
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
-
-        encoder_outputs = self.encoder(
-            inputs_embeds=hidden_states,
-            attention_mask=None,
-            causal_attention_mask=None,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        last_hidden_state = encoder_outputs[0]
-        last_hidden_state = self.final_layer_norm(last_hidden_state)
-
-        # text_embeds.shape = [batch_size, sequence_length, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        pooled_output = last_hidden_state[torch.arange(last_hidden_state.shape[0]), input_ids.argmax(dim=-1)]
-
-        if not return_dict:
-            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPooling(
-            last_hidden_state=last_hidden_state,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-        )
-
-    def _build_causal_attention_mask(self, bsz, seq_len):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(bsz, seq_len, seq_len)
-        mask.fill_(torch.tensor(float("-inf")))
-        mask.triu_(1)  # zero out the lower diagonal
-        mask = mask.unsqueeze(1)  # expand mask
-        return mask
-
-
-class CLIPTextModel(CLIPPreTrainedModel):
-    config_class = CLIPTextConfig
-
-    def __init__(self, config: CLIPTextConfig):
-        super().__init__(config)
-        self.text_model = CLIPTextTransformer(config)
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self) -> nn.Module:
-        return self.text_model.embeddings.token_embedding
-
-    def set_input_embeddings(self, value):
-        self.text_model.embeddings.token_embedding = value
-
-    @add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPTextConfig)
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import CLIPTokenizer, CLIPTextModel
-
-        >>> model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
-        >>> tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
-
-        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
-
-        >>> outputs = model(**inputs)
-        >>> last_hidden_state = outputs.last_hidden_state
-        >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
-        ```"""
-        return self.text_model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )

modeling_glide.py

@@ -1,923 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The OpenAI Team Authors and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch CLIP model."""
-
-import math
-from dataclasses import dataclass
-from typing import Any, Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.utils.checkpoint
-from torch import nn
-
-import tqdm
-from diffusers import (
-    ClassifierFreeGuidanceScheduler,
-    DiffusionPipeline,
-    GlideDDIMScheduler,
-    GLIDESuperResUNetModel,
-    GLIDETextToImageUNetModel,
-)
-from transformers import CLIPConfig, CLIPModel, CLIPTextConfig, CLIPVisionConfig, GPT2Tokenizer
-from transformers.activations import ACT2FN
-from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (
-    ModelOutput,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-
-
-#####################
-# START OF THE CLIP MODEL COPY-PASTE (with a modified attention module)
-#####################
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "fusing/glide-base"
-
-CLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "fusing/glide-base",
-    # See all CLIP models at https://huggingface.co/models?filter=clip
-]
-
-
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-
-
-# contrastive loss function, adapted from
-# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
-def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
-    return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))
-
-
-def clip_loss(similarity: torch.Tensor) -> torch.Tensor:
-    caption_loss = contrastive_loss(similarity)
-    image_loss = contrastive_loss(similarity.T)
-    return (caption_loss + image_loss) / 2.0
-
-
-@dataclass
-class CLIPOutput(ModelOutput):
-    """
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
-            Contrastive loss for image-text similarity.
-        logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
-            The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
-            similarity scores.
-        logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
-            The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
-            similarity scores.
-        text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
-            The text embeddings obtained by applying the projection layer to the pooled output of [`CLIPTextModel`].
-        image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
-            The image embeddings obtained by applying the projection layer to the pooled output of [`CLIPVisionModel`].
-        text_model_output(`BaseModelOutputWithPooling`):
-            The output of the [`CLIPTextModel`].
-        vision_model_output(`BaseModelOutputWithPooling`):
-            The output of the [`CLIPVisionModel`].
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits_per_image: torch.FloatTensor = None
-    logits_per_text: torch.FloatTensor = None
-    text_embeds: torch.FloatTensor = None
-    image_embeds: torch.FloatTensor = None
-    text_model_output: BaseModelOutputWithPooling = None
-    vision_model_output: BaseModelOutputWithPooling = None
-
-    def to_tuple(self) -> Tuple[Any]:
-        return tuple(
-            self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
-            for k in self.keys()
-        )
-
-
-class CLIPVisionEmbeddings(nn.Module):
-    def __init__(self, config: CLIPVisionConfig):
-        super().__init__()
-        self.config = config
-        self.embed_dim = config.hidden_size
-        self.image_size = config.image_size
-        self.patch_size = config.patch_size
-
-        self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
-
-        self.patch_embedding = nn.Conv2d(
-            in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False
-        )
-
-        self.num_patches = (self.image_size // self.patch_size) ** 2
-        self.num_positions = self.num_patches + 1
-        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
-        self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)))
-
-    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
-        batch_size = pixel_values.shape[0]
-        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, width, grid, grid]
-        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
-
-        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
-        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
-        embeddings = embeddings + self.position_embedding(self.position_ids)
-        return embeddings
-
-
-class CLIPTextEmbeddings(nn.Module):
-    def __init__(self, config: CLIPTextConfig):
-        super().__init__()
-        embed_dim = config.hidden_size
-
-        self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
-        self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
-        self.use_padding_embeddings = config.use_padding_embeddings
-        if self.use_padding_embeddings:
-            self.padding_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, :seq_length]
-
-        if inputs_embeds is None:
-            inputs_embeds = self.token_embedding(input_ids)
-
-        position_embeddings = self.position_embedding(position_ids)
-        embeddings = inputs_embeds + position_embeddings
-
-        if self.use_padding_embeddings and attention_mask is not None:
-            padding_embeddings = self.padding_embedding(position_ids)
-            embeddings = torch.where(attention_mask.bool().unsqueeze(-1), embeddings, padding_embeddings)
-
-        return embeddings
-
-
-class CLIPAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.embed_dim = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.embed_dim // self.num_heads
-        if self.head_dim * self.num_heads != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
-                f" {self.num_heads})."
-            )
-        self.scale = 1 / math.sqrt(math.sqrt(self.head_dim))
-
-        self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3)
-        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        causal_attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        bsz, tgt_len, embed_dim = hidden_states.size()
-
-        qkv_states = self.qkv_proj(hidden_states)
-        qkv_states = qkv_states.view(bsz, tgt_len, self.num_heads, -1)
-        query_states, key_states, value_states = torch.split(qkv_states, self.head_dim, dim=-1)
-
-        attn_weights = torch.einsum("bthc,bshc->bhts", query_states * self.scale, key_states * self.scale)
-
-        wdtype = attn_weights.dtype
-        attn_weights = nn.functional.softmax(attn_weights.float(), dim=-1).type(wdtype)
-
-        attn_output = torch.einsum("bhts,bshc->bthc", attn_weights, value_states)
-        attn_output = attn_output.reshape(bsz, tgt_len, -1)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights
-
-
-class CLIPMLP(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.activation_fn = ACT2FN[config.hidden_act]
-        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
-        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.fc1(hidden_states)
-        hidden_states = self.activation_fn(hidden_states)
-        hidden_states = self.fc2(hidden_states)
-        return hidden_states
-
-
-class CLIPEncoderLayer(nn.Module):
-    def __init__(self, config: CLIPConfig):
-        super().__init__()
-        self.embed_dim = config.hidden_size
-        self.self_attn = CLIPAttention(config)
-        self.layer_norm1 = nn.LayerNorm(self.embed_dim)
-        self.mlp = CLIPMLP(config)
-        self.layer_norm2 = nn.LayerNorm(self.embed_dim)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor,
-        causal_attention_mask: torch.Tensor,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-                `(config.encoder_attention_heads,)`.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-        """
-        residual = hidden_states
-
-        hidden_states = self.layer_norm1(hidden_states)
-        hidden_states, attn_weights = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            causal_attention_mask=causal_attention_mask,
-            output_attentions=output_attentions,
-        )
-        hidden_states = residual + hidden_states
-
-        residual = hidden_states
-        hidden_states = self.layer_norm2(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs
-
-
-class CLIPPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = CLIPConfig
-    base_model_prefix = "clip"
-    supports_gradient_checkpointing = True
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        factor = self.config.initializer_factor
-        if isinstance(module, CLIPTextEmbeddings):
-            module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
-            module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
-            if hasattr(module, "padding_embedding"):
-                module.padding_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
-        elif isinstance(module, CLIPVisionEmbeddings):
-            factor = self.config.initializer_factor
-            nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
-            nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
-            nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor)
-        elif isinstance(module, CLIPAttention):
-            factor = self.config.initializer_factor
-            in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
-            out_proj_std = (module.embed_dim**-0.5) * factor
-            nn.init.normal_(module.qkv_proj.weight, std=in_proj_std)
-            nn.init.normal_(module.out_proj.weight, std=out_proj_std)
-        elif isinstance(module, CLIPMLP):
-            factor = self.config.initializer_factor
-            in_proj_std = (
-                (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
-            )
-            fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
-            nn.init.normal_(module.fc1.weight, std=fc_std)
-            nn.init.normal_(module.fc2.weight, std=in_proj_std)
-        elif isinstance(module, CLIPModel):
-            nn.init.normal_(
-                module.text_projection.weight,
-                std=module.text_embed_dim**-0.5 * self.config.initializer_factor,
-            )
-            nn.init.normal_(
-                module.visual_projection.weight,
-                std=module.vision_embed_dim**-0.5 * self.config.initializer_factor,
-            )
-
-        if isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        if isinstance(module, nn.Linear) and module.bias is not None:
-            module.bias.data.zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, CLIPEncoder):
-            module.gradient_checkpointing = value
-
-
-CLIP_START_DOCSTRING = r"""
-    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
-    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
-    behavior.
-
-    Parameters:
-        config ([`CLIPConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-CLIP_TEXT_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-CLIP_VISION_INPUTS_DOCSTRING = r"""
-    Args:
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
-            [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-CLIP_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
-            [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
-        return_loss (`bool`, *optional*):
-            Whether or not to return the contrastive loss.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-class CLIPEncoder(nn.Module):
-    """
-    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
-    [`CLIPEncoderLayer`].
-
-    Args:
-        config: CLIPConfig
-    """
-
-    def __init__(self, config: CLIPConfig):
-        super().__init__()
-        self.config = config
-        self.layers = nn.ModuleList([CLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        inputs_embeds,
-        attention_mask: Optional[torch.Tensor] = None,
-        causal_attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutput]:
-        r"""
-        Args:
-            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
-                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
-                than the model's internal embedding lookup matrix.
-            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Causal mask for the text model. Mask values selected in `[0, 1]`:
-
-                - 1 for tokens that are **not masked**,
-                - 0 for tokens that are **masked**.
-
-                [What are attention masks?](../glossary#attention-mask)
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
-                for more detail.
-            return_dict (`bool`, *optional*):
-                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        encoder_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
-        hidden_states = inputs_embeds
-        for idx, encoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                encoder_states = encoder_states + (hidden_states,)
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(encoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    causal_attention_mask,
-                )
-            else:
-                layer_outputs = encoder_layer(
-                    hidden_states,
-                    attention_mask,
-                    causal_attention_mask,
-                    output_attentions=output_attentions,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            encoder_states = encoder_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
-        )
-
-
-class CLIPTextTransformer(nn.Module):
-    def __init__(self, config: CLIPTextConfig):
-        super().__init__()
-        self.config = config
-        embed_dim = config.hidden_size
-        self.embeddings = CLIPTextEmbeddings(config)
-        self.encoder = CLIPEncoder(config)
-        self.final_layer_norm = nn.LayerNorm(embed_dim)
-
-    @add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPTextConfig)
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
-        r"""
-        Returns:
-
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if input_ids is None:
-            raise ValueError("You have to specify either input_ids")
-
-        input_shape = input_ids.size()
-        input_ids = input_ids.view(-1, input_shape[-1])
-
-        hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids, attention_mask=attention_mask)
-
-        bsz, seq_len = input_shape
-        # CLIP's text model uses causal mask, prepare it here.
-        # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
-        causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len).to(hidden_states.device)
-
-        # expand attention_mask
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
-
-        encoder_outputs = self.encoder(
-            inputs_embeds=hidden_states,
-            attention_mask=None,
-            causal_attention_mask=None,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        last_hidden_state = encoder_outputs[0]
-        last_hidden_state = self.final_layer_norm(last_hidden_state)
-
-        # text_embeds.shape = [batch_size, sequence_length, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        pooled_output = last_hidden_state[torch.arange(last_hidden_state.shape[0]), input_ids.argmax(dim=-1)]
-
-        if not return_dict:
-            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPooling(
-            last_hidden_state=last_hidden_state,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-        )
-
-    def _build_causal_attention_mask(self, bsz, seq_len):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(bsz, seq_len, seq_len)
-        mask.fill_(torch.tensor(float("-inf")))
-        mask.triu_(1)  # zero out the lower diagonal
-        mask = mask.unsqueeze(1)  # expand mask
-        return mask
-
-
-class CLIPTextModel(CLIPPreTrainedModel):
-    config_class = CLIPTextConfig
-
-    def __init__(self, config: CLIPTextConfig):
-        super().__init__(config)
-        self.text_model = CLIPTextTransformer(config)
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self) -> nn.Module:
-        return self.text_model.embeddings.token_embedding
-
-    def set_input_embeddings(self, value):
-        self.text_model.embeddings.token_embedding = value
-
-    @add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPTextConfig)
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import CLIPTokenizer, CLIPTextModel
-
-        >>> model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
-        >>> tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
-
-        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
-
-        >>> outputs = model(**inputs)
-        >>> last_hidden_state = outputs.last_hidden_state
-        >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
-        ```"""
-        return self.text_model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-
-#####################
-# END OF THE CLIP MODEL COPY-PASTE
-#####################
-
-
-def _extract_into_tensor(arr, timesteps, broadcast_shape):
-    """
-    Extract values from a 1-D numpy array for a batch of indices.
-
-    :param arr: the 1-D numpy array.
-    :param timesteps: a tensor of indices into the array to extract.
-    :param broadcast_shape: a larger shape of K dimensions with the batch
-                            dimension equal to the length of timesteps.
-    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
-    """
-    res = torch.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
-    while len(res.shape) < len(broadcast_shape):
-        res = res[..., None]
-    return res + torch.zeros(broadcast_shape, device=timesteps.device)
-
-
-class GLIDE(DiffusionPipeline):
-    def __init__(
-        self,
-        text_unet: GLIDETextToImageUNetModel,
-        text_noise_scheduler: ClassifierFreeGuidanceScheduler,
-        text_encoder: CLIPTextModel,
-        tokenizer: GPT2Tokenizer,
-        upscale_unet: GLIDESuperResUNetModel,
-        upscale_noise_scheduler: GlideDDIMScheduler,
-    ):
-        super().__init__()
-        self.register_modules(
-            text_unet=text_unet,
-            text_noise_scheduler=text_noise_scheduler,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            upscale_unet=upscale_unet,
-            upscale_noise_scheduler=upscale_noise_scheduler,
-        )
-
-    def q_posterior_mean_variance(self, scheduler, x_start, x_t, t):
-        """
-        Compute the mean and variance of the diffusion posterior:
-
-            q(x_{t-1} | x_t, x_0)
-
-        """
-        assert x_start.shape == x_t.shape
-        posterior_mean = (
-            _extract_into_tensor(scheduler.posterior_mean_coef1, t, x_t.shape) * x_start
-            + _extract_into_tensor(scheduler.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = _extract_into_tensor(scheduler.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = _extract_into_tensor(scheduler.posterior_log_variance_clipped, t, x_t.shape)
-        assert (
-            posterior_mean.shape[0]
-            == posterior_variance.shape[0]
-            == posterior_log_variance_clipped.shape[0]
-            == x_start.shape[0]
-        )
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, model, scheduler, x, t, transformer_out=None, low_res=None, clip_denoised=True):
-        """
-        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
-        the initial x, x_0.
-
-        :param model: the model, which takes a signal and a batch of timesteps
-                      as input.
-        :param x: the [N x C x ...] tensor at time t.
-        :param t: a 1-D Tensor of timesteps.
-        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
-        :param model_kwargs: if not None, a dict of extra keyword arguments to
-            pass to the model. This can be used for conditioning.
-        :return: a dict with the following keys:
-                 - 'mean': the model mean output.
-                 - 'variance': the model variance output.
-                 - 'log_variance': the log of 'variance'.
-                 - 'pred_xstart': the prediction for x_0.
-        """
-
-        B, C = x.shape[:2]
-        assert t.shape == (B,)
-        if transformer_out is None:
-            # super-res model
-            model_output = model(x, t, low_res)
-        else:
-            # text2image model
-            model_output = model(x, t, transformer_out)
-
-        assert model_output.shape == (B, C * 2, *x.shape[2:])
-        model_output, model_var_values = torch.split(model_output, C, dim=1)
-        min_log = _extract_into_tensor(scheduler.posterior_log_variance_clipped, t, x.shape)
-        max_log = _extract_into_tensor(np.log(scheduler.betas), t, x.shape)
-        # The model_var_values is [-1, 1] for [min_var, max_var].
-        frac = (model_var_values + 1) / 2
-        model_log_variance = frac * max_log + (1 - frac) * min_log
-        model_variance = torch.exp(model_log_variance)
-
-        pred_xstart = self._predict_xstart_from_eps(scheduler, x_t=x, t=t, eps=model_output)
-        if clip_denoised:
-            pred_xstart = pred_xstart.clamp(-1, 1)
-        model_mean, _, _ = self.q_posterior_mean_variance(scheduler, x_start=pred_xstart, x_t=x, t=t)
-
-        assert model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
-        return model_mean, model_variance, model_log_variance, pred_xstart
-
-    def _predict_xstart_from_eps(self, scheduler, x_t, t, eps):
-        assert x_t.shape == eps.shape
-        return (
-            _extract_into_tensor(scheduler.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
-            - _extract_into_tensor(scheduler.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
-        )
-
-    def _predict_eps_from_xstart(self, scheduler, x_t, t, pred_xstart):
-        return (
-            _extract_into_tensor(scheduler.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart
-        ) / _extract_into_tensor(scheduler.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    @torch.no_grad()
-    def __call__(self, prompt, generator=None, torch_device=None):
-        torch_device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        self.text_unet.to(torch_device)
-        self.text_encoder.to(torch_device)
-        self.upscale_unet.to(torch_device)
-
-        # Create a classifier-free guidance sampling function
-        guidance_scale = 3.0
-
-        def text_model_fn(x_t, ts, transformer_out, **kwargs):
-            half = x_t[: len(x_t) // 2]
-            combined = torch.cat([half, half], dim=0)
-            model_out = self.text_unet(combined, ts, transformer_out, **kwargs)
-            eps, rest = model_out[:, :3], model_out[:, 3:]
-            cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
-            half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
-            eps = torch.cat([half_eps, half_eps], dim=0)
-            return torch.cat([eps, rest], dim=1)
-
-        # 1. Sample gaussian noise
-        batch_size = 2  # second image is empty for classifier-free guidance
-        image = self.text_noise_scheduler.sample_noise(
-            (batch_size, self.text_unet.in_channels, 64, 64), device=torch_device, generator=generator
-        )
-
-        # 2. Encode tokens
-        # an empty input is needed to guide the model away from (
-        inputs = self.tokenizer([prompt, ""], padding="max_length", max_length=128, return_tensors="pt")
-        input_ids = inputs["input_ids"].to(torch_device)
-        attention_mask = inputs["attention_mask"].to(torch_device)
-        transformer_out = self.text_encoder(input_ids, attention_mask).last_hidden_state
-
-        # 3. Run the text2image generation step
-        num_timesteps = len(self.text_noise_scheduler)
-        for i in tqdm.tqdm(reversed(range(num_timesteps)), total=num_timesteps):
-            t = torch.tensor([i] * image.shape[0], device=torch_device)
-            mean, variance, log_variance, pred_xstart = self.p_mean_variance(
-                text_model_fn, self.text_noise_scheduler, image, t, transformer_out=transformer_out
-            )
-            noise = self.text_noise_scheduler.sample_noise(image.shape, device=torch_device, generator=generator)
-            nonzero_mask = (t != 0).float().view(-1, *([1] * (len(image.shape) - 1)))  # no noise when t == 0
-            image = mean + nonzero_mask * torch.exp(0.5 * log_variance) * noise
-
-        # 4. Run the upscaling step
-        batch_size = 1
-        image = image[:1]
-        low_res = ((image + 1) * 127.5).round() / 127.5 - 1
-        eta = 0.0
-
-        # Tune this parameter to control the sharpness of 256x256 images.
-        # A value of 1.0 is sharper, but sometimes results in grainy artifacts.
-        upsample_temp = 0.997
-
-        image = (
-            self.upscale_noise_scheduler.sample_noise(
-                (batch_size, 3, 256, 256), device=torch_device, generator=generator
-            )
-            * upsample_temp
-        )
-
-        num_timesteps = len(self.upscale_noise_scheduler)
-        for t in tqdm.tqdm(
-            reversed(range(len(self.upscale_noise_scheduler))), total=len(self.upscale_noise_scheduler)
-        ):
-            # i) define coefficients for time step t
-            clipped_image_coeff = 1 / torch.sqrt(self.upscale_noise_scheduler.get_alpha_prod(t))
-            clipped_noise_coeff = torch.sqrt(1 / self.upscale_noise_scheduler.get_alpha_prod(t) - 1)
-            image_coeff = (
-                (1 - self.upscale_noise_scheduler.get_alpha_prod(t - 1))
-                * torch.sqrt(self.upscale_noise_scheduler.get_alpha(t))
-                / (1 - self.upscale_noise_scheduler.get_alpha_prod(t))
-            )
-            clipped_coeff = (
-                torch.sqrt(self.upscale_noise_scheduler.get_alpha_prod(t - 1))
-                * self.upscale_noise_scheduler.get_beta(t)
-                / (1 - self.upscale_noise_scheduler.get_alpha_prod(t))
-            )
-
-            # ii) predict noise residual
-            time_input = torch.tensor([t] * image.shape[0], device=torch_device)
-            model_output = self.upscale_unet(image, time_input, low_res)
-            noise_residual, pred_variance = torch.split(model_output, 3, dim=1)
-
-            # iii) compute predicted image from residual
-            # See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
-            pred_mean = clipped_image_coeff * image - clipped_noise_coeff * noise_residual
-            pred_mean = torch.clamp(pred_mean, -1, 1)
-            prev_image = clipped_coeff * pred_mean + image_coeff * image
-
-            # iv) sample variance
-            prev_variance = self.upscale_noise_scheduler.sample_variance(
-                t, prev_image.shape, device=torch_device, generator=generator
-            )
-
-            # v) sample  x_{t-1} ~ N(prev_image, prev_variance)
-            sampled_prev_image = prev_image + prev_variance
-            image = sampled_prev_image
-
-        image = image.permute(0, 2, 3, 1)
-
-        return image