





'''simple docstring'''

from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowerCAmelCase							:     List[Any]		     =logging.get_logger(__name__)  # pylint: disable=invalid-name






class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__    ):
 @register_to_config
 def __init__(    self				:Tuple						,	lowercase_				:bool						,	lowercase_				:Optional[int] = None						,	lowercase_				:Optional[int] = None							)->							Any:
     super().__init__()

     A__    =      learnable

     if self.learnable:
         assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
         assert length is not None, "learnable=True requires `length` to be set"

         A__    =      torch.zeros(lowercase_						,	lowercase_							)
     else:
         A__    =      None

     A__    =      torch.nn.Parameter(lowercase_							)






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    42
 __lowercase       		=    42
 __lowercase       		=    42
 __lowercase       		=    42
 __lowercase       		=    42
 __lowercase       		=    42
 def __init__(    self				:List[Any]						,	lowercase_				:VQModel						,	lowercase_				:CLIPTextModel						,	lowercase_				:CLIPTokenizer						,	lowercase_				:TransformeraDModel						,	lowercase_				:VQDiffusionScheduler						,	lowercase_				:LearnedClassifierFreeSamplingEmbeddings						,	)->							Optional[Any]:
     super().__init__()

     self.register_modules(
         vqvae=lowercase_						,	transformer=lowercase_						,	text_encoder=lowercase_						,	tokenizer=lowercase_						,	scheduler=lowercase_						,	learned_classifier_free_sampling_embeddings=lowercase_						,	)
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Union[str, Any]						,	lowercase_				:int						,	lowercase_				:Optional[int]							)->							List[str]:
     A__    =      len(lowercase_							) if isinstance(lowercase_						,	lowercase_							) else 1

     # get prompt text embeddings
     A__    =      self.tokenizer(
         lowercase_						,	padding="max_length"						,	max_length=self.tokenizer.model_max_length						,	return_tensors="pt"						,	)
     A__    =      text_inputs.input_ids

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
         A__    =      self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
         logger.warning(
             "The following part of your input was truncated because CLIP can only handle sequences up to"
             F" {self.tokenizer.model_max_length} tokens: {removed_text}"							)
         A__    =      text_input_ids[:, : self.tokenizer.model_max_length]
     A__    =      self.text_encoder(text_input_ids.to(self.device							)							)[0]

     # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
     # While CLIP does normalize the pooled output of the text transformer when combining
     # the image and text embeddings, CLIP does not directly normalize the last hidden state.
     #
     # CLIP normalizing the pooled output.
     # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
     A__    =      prompt_embeds / prompt_embeds.norm(dim=-1						,	keepdim=lowercase_							)

     # duplicate text embeddings for each generation per prompt
     A__    =      prompt_embeds.repeat_interleave(lowercase_						,	dim=0							)

     if do_classifier_free_guidance:
         if self.learned_classifier_free_sampling_embeddings.learnable:
             A__    =      self.learned_classifier_free_sampling_embeddings.embeddings
             A__    =      negative_prompt_embeds.unsqueeze(0							).repeat(lowercase_						,	1						,	1							)
         else:
             A__    =      [""] * batch_size

             A__    =      text_input_ids.shape[-1]
             A__    =      self.tokenizer(
                 lowercase_						,	padding="max_length"						,	max_length=lowercase_						,	truncation=lowercase_						,	return_tensors="pt"						,	)
             A__    =      self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
             # See comment for normalizing text embeddings
             A__    =      negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1						,	keepdim=lowercase_							)

         # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
         A__    =      negative_prompt_embeds.shape[1]
         A__    =      negative_prompt_embeds.repeat(1						,	lowercase_						,	1							)
         A__    =      negative_prompt_embeds.view(batch_size * num_images_per_prompt						,	lowercase_						,	-1							)

         # For classifier free guidance, we need to do two forward passes.
         # Here we concatenate the unconditional and text embeddings into a single batch
         # to avoid doing two forward passes
         A__    =      torch.cat([negative_prompt_embeds, prompt_embeds]							)

     return prompt_embeds
 @torch.no_grad()
 def __call__(    self				:str						,	lowercase_				:Union[str, List[str]]						,	lowercase_				:int = 1_00						,	lowercase_				:float = 5.0						,	lowercase_				:float = 1.0						,	lowercase_				:int = 1						,	lowercase_				:Optional[Union[torch.Generator, List[torch.Generator]]] = None						,	lowercase_				:Optional[torch.FloatTensor] = None						,	lowercase_				:Optional[str] = "pil"						,	lowercase_				:bool = True						,	lowercase_				:Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,	lowercase_				:int = 1						,	)->							Union[ImagePipelineOutput, Tuple]:
     if isinstance(lowercase_						,	lowercase_							):
         A__    =      1
     elif isinstance(lowercase_						,	lowercase_							):
         A__    =      len(lowercase_							)
     else:
         raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowercase_							)}"							)

     A__    =      batch_size * num_images_per_prompt

     A__    =      guidance_scale > 1.0

     A__    =      self._encode_prompt(lowercase_						,	lowercase_						,	lowercase_							)

     if (callback_steps is None) or (
         callback_steps is not None and (not isinstance(lowercase_						,	lowercase_							) or callback_steps <= 0)
     ):
         raise ValueError(
             F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
             F" {type(lowercase_							)}."							)

     # get the initial completely masked latents unless the user supplied it

     A__    =      (batch_size, self.transformer.num_latent_pixels)
     if latents is None:
         A__    =      self.transformer.num_vector_embeds - 1
         A__    =      torch.full(lowercase_						,	lowercase_							).to(self.device							)
     else:
         if latents.shape != latents_shape:
             raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"							)
         if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
             raise ValueError(
                 "Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
                 F" {self.transformer.num_vector_embeds - 1} (inclusive)."							)
         A__    =      latents.to(self.device							)

     # set timesteps
     self.scheduler.set_timesteps(lowercase_						,	device=self.device							)

     A__    =      self.scheduler.timesteps.to(self.device							)

     A__    =      latents

     for i, t in enumerate(self.progress_bar(lowercase_							)							):
         # expand the sample if we are doing classifier free guidance
         A__    =      torch.cat([sample] * 2							) if do_classifier_free_guidance else sample

         # predict the un-noised image
         # model_output == `log_p_x_0`
         A__    =      self.transformer(lowercase_						,	encoder_hidden_states=lowercase_						,	timestep=lowercase_							).sample

         if do_classifier_free_guidance:
             A__,     A__    =      model_output.chunk(2							)
             A__    =      model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
             model_output -= torch.logsumexp(lowercase_						,	dim=1						,	keepdim=lowercase_							)

         A__    =      self.truncate(lowercase_						,	lowercase_							)

         # remove `log(0)`'s (`-inf`s)
         A__    =      model_output.clamp(-70							)

         # compute the previous noisy sample x_t -> x_t-1
         A__    =      self.scheduler.step(lowercase_						,	timestep=lowercase_						,	sample=lowercase_						,	generator=lowercase_							).prev_sample

         # call the callback, if provided
         if callback is not None and i % callback_steps == 0:
             callback(lowercase_						,	lowercase_						,	lowercase_							)

     A__    =      self.vqvae.config.vq_embed_dim
     A__    =      (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
     A__    =      self.vqvae.quantize.get_codebook_entry(lowercase_						,	shape=lowercase_							)
     A__    =      self.vqvae.decode(lowercase_						,	force_not_quantize=lowercase_							).sample

     A__    =      (image / 2 + 0.5).clamp(0						,	1							)
     A__    =      image.cpu().permute(0						,	2						,	3						,	1							).numpy()

     if output_type == "pil":
         A__    =      self.numpy_to_pil(lowercase_							)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=lowercase_							)



 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:torch.FloatTensor						,	lowercase_				:float							)->							torch.FloatTensor:
     A__,     A__    =      torch.sort(lowercase_						,	1						,	descending=lowercase_							)
     A__    =      torch.exp(lowercase_							)
     A__    =      sorted_p_x_0.cumsum(dim=1							) < truncation_rate

     # Ensure that at least the largest probability is not zeroed out
     A__    =      torch.full_like(keep_mask[:, 0:1, :]						,	lowercase_							)
     A__    =      torch.cat((all_true, keep_mask)						,	dim=1							)
     A__    =      keep_mask[:, :-1, :]

     A__    =      keep_mask.gather(1						,	indices.argsort(1							)							)

     A__    =      log_p_x_0.clone()

     A__    =      -torch.inf  # -inf = log(0)

     return rv










'''simple docstring'''

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
      TimesformerForVideoClassification,
      TimesformerModel,
  )
  from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from transformers import VideoMAEImageProcessor






class        UpperCAmelCase     :
 def __init__(    self				:Optional[Any]						,	lowercase_				:int						,	lowercase_				:Union[str, Any]=13						,	lowercase_				:Union[str, Any]=10						,	lowercase_				:Any=3						,	lowercase_				:Tuple=2						,	lowercase_				:List[Any]=2						,	lowercase_				:int=True						,	lowercase_				:int=True						,	lowercase_				:List[str]=32						,	lowercase_				:Dict=5						,	lowercase_				:List[Any]=4						,	lowercase_				:List[Any]=37						,	lowercase_				:List[Any]="gelu"						,	lowercase_				:int=0.1						,	lowercase_				:List[Any]=0.1						,	lowercase_				:List[Any]=10						,	lowercase_				:int=0.0_2						,	lowercase_				:Union[str, Any]="divided_space_time"						,	lowercase_				:Tuple=None						,	)->							Tuple:
     A__    =      parent
     A__    =      batch_size
     A__    =      image_size
     A__    =      num_channels
     A__    =      patch_size
     A__    =      num_frames
     A__    =      is_training
     A__    =      use_labels
     A__    =      hidden_size
     A__    =      num_hidden_layers
     A__    =      num_attention_heads
     A__    =      intermediate_size
     A__    =      hidden_act
     A__    =      hidden_dropout_prob
     A__    =      attention_probs_dropout_prob
     A__    =      attention_type
     A__    =      initializer_range
     A__    =      scope
     A__    =      num_labels

     # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
     A__    =      (image_size // patch_size) ** 2
     A__    =      (num_frames) * self.num_patches_per_frame + 1
 def 					UpperCAmelCase_							(    self				:str							)->							str:
     A__    =      floats_tensor(
         [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]							)

     A__    =      None
     if self.use_labels:
         A__    =      ids_tensor([self.batch_size]						,	self.num_labels							)

     A__    =      self.get_config()

     return config, pixel_values, labels
 def 					UpperCAmelCase_							(    self				:int							)->							Any:
     A__    =      TimesformerConfig(
         image_size=self.image_size						,	patch_size=self.patch_size						,	num_channels=self.num_channels						,	num_frames=self.num_frames						,	hidden_size=self.hidden_size						,	num_hidden_layers=self.num_hidden_layers						,	num_attention_heads=self.num_attention_heads						,	intermediate_size=self.intermediate_size						,	hidden_act=self.hidden_act						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	initializer_range=self.initializer_range						,	attention_type=self.attention_type						,	)
     A__    =      self.num_labels
     return config
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:List[str]						,	lowercase_				:List[Any]						,	lowercase_				:Tuple							)->							Optional[int]:
     A__    =      TimesformerModel(config=lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:Tuple						,	lowercase_				:Tuple						,	lowercase_				:Dict							)->							Tuple:
     A__    =      TimesformerForVideoClassification(lowercase_							)
     model.to(lowercase_							)
     model.eval()

     A__    =      model(lowercase_							)

     # verify the logits shape
     A__    =      torch.Size((self.batch_size, self.num_labels)							)
     self.parent.assertEqual(result.logits.shape						,	lowercase_							)



 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							str:
     A__    =      self.prepare_config_and_inputs()
     A__,     A__,     A__    =      config_and_inputs
     A__    =      {"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
 __lowercase       		=    (
     {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
     if is_torch_available()
     else {}
 )

 __lowercase       		=    False
 __lowercase       		=    False
 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Optional[int]:
     A__    =      TimesformerModelTester(self							)
     A__    =      ConfigTester(
         self						,	config_class=lowercase_						,	has_text_modality=lowercase_						,	hidden_size=37							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:int						,	lowercase_				:Dict						,	lowercase_				:int=False							)->							str:
     A__    =      copy.deepcopy(lowercase_							)

     if return_labels:
         if model_class in get_values(lowercase_							):
             A__    =      torch.zeros(
                 self.model_tester.batch_size						,	dtype=torch.long						,	device=lowercase_							)

     return inputs_dict
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							List[Any]:
     self.config_tester.run_common_tests()
 @unittest.skip(reason="TimeSformer does not use inputs_embeds"							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     pass
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[Any]:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__    =      model_class(lowercase_							)
         self.assertIsInstance(model.get_input_embeddings()						,	(nn.Module)							)
         A__    =      model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowercase_						,	nn.Linear							)							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Dict:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__    =      model_class(lowercase_							)
         A__    =      inspect.signature(model.forward							)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         A__    =      [*signature.parameters.keys()]

         A__    =      ["pixel_values"]
         self.assertListEqual(arg_names[:1]						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_video_classification(*lowercase_							)
 @slow
 def 					UpperCAmelCase_							(    self				:Any							)->							List[Any]:
     for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         A__    =      TimesformerModel.from_pretrained(lowercase_							)
         self.assertIsNotNone(lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     if not self.has_attentions:
         pass

     else:
         A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()
         A__    =      True

         for model_class in self.all_model_classes:
             A__    =      self.model_tester.seq_length
             A__    =      self.model_tester.num_frames

             A__    =      True
             A__    =      False
             A__    =      True
             A__    =      model_class(lowercase_							)
             model.to(lowercase_							)
             model.eval()
             with torch.no_grad():
                 A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)
             A__    =      outputs.attentions
             self.assertEqual(len(lowercase_							)						,	self.model_tester.num_hidden_layers							)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             A__    =      True
             A__    =      model_class(lowercase_							)
             model.to(lowercase_							)
             model.eval()
             with torch.no_grad():
                 A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)
             A__    =      outputs.attentions
             self.assertEqual(len(lowercase_							)						,	self.model_tester.num_hidden_layers							)

             # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
             self.assertListEqual(
                 list(attentions[0].shape[-3:]							)						,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]						,	)
             A__    =      len(lowercase_							)

             # Check attention is always last and order is fine
             A__    =      True
             A__    =      True
             A__    =      model_class(lowercase_							)
             model.to(lowercase_							)
             model.eval()
             with torch.no_grad():
                 A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)

             self.assertEqual(out_len + 1						,	len(lowercase_							)							)

             A__    =      outputs.attentions

             self.assertEqual(len(lowercase_							)						,	self.model_tester.num_hidden_layers							)

             # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]							)						,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]						,	)



 def 					UpperCAmelCase_							(    self				:List[Any]							)->							List[str]:
     def check_hidden_states_output(lowercase_				:Dict						,	lowercase_				:int						,	lowercase_				:List[Any]							):
         A__    =      model_class(lowercase_							)
         model.to(lowercase_							)
         model.eval()

         with torch.no_grad():
             A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)

         A__    =      outputs.hidden_states
         A__    =      self.model_tester.num_hidden_layers + 1
         self.assertEqual(len(lowercase_							)						,	lowercase_							)

         A__    =      self.model_tester.seq_length

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]							)						,	[seq_length, self.model_tester.hidden_size]						,	)

     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__    =      True
         check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_							)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         A__    =      True

         check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_							)





def 						UpperCamelCase    (							):
    A__    =      hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video"						,	filename="eating_spaghetti.npy"						,	repo_type="dataset"	)
    A__    =      np.load(_lowerCamelCase	)
    return list(_lowerCamelCase	)






@require_torch
@require_vision
class        UpperCAmelCase     (	unittest.TestCase    ):
 @cached_property
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							int:
     # logits were tested with a different mean and std, so we use the same here
     return (
         VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]						,	image_std=[0.5, 0.5, 0.5]							)
         if is_vision_available()
         else None
     )



 @slow
 def 					UpperCAmelCase_							(    self				:int							)->							Any:
     A__    =      TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"							).to(
         lowercase_							)

     A__    =      self.default_image_processor
     A__    =      prepare_video()
     A__    =      image_processor(video[:8]						,	return_tensors="pt"							).to(lowercase_							)

     # forward pass
     with torch.no_grad():
         A__    =      model(**lowercase_							)

     # verify the logits
     A__    =      torch.Size((1, 4_00)							)
     self.assertEqual(outputs.logits.shape						,	lowercase_							)

     A__    =      torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]							).to(lowercase_							)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]						,	lowercase_						,	atol=1E-4							)							)



