

import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





@skip_mps
class A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					str      =						TextToVideoSDPipeline
 _lowercase     :					str      =						TEXT_TO_IMAGE_PARAMS
 _lowercase     :					Dict      =						TEXT_TO_IMAGE_BATCH_PARAMS
 # No `output_type`.
 _lowercase     :					Union[str, Any]      =						frozenset(
     [
         '''num_inference_steps''',
         '''generator''',
         '''latents''',
         '''return_dict''',
         '''callback''',
         '''callback_steps''',
     ] )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					Union[str, Any]        =    UNetaDConditionModel(
          block_out_channels=(3_2, 6_4, 6_4, 6_4)     ,						layers_per_block=2     ,						sample_size=3_2     ,						in_channels=4     ,						out_channels=4     ,						down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")     ,						up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")     ,						cross_attention_dim=3_2     ,						attention_head_dim=4     ,						)
      _lowerCAmelCase   :					Any        =    DDIMScheduler(
          beta_start=0.00085     ,						beta_end=0.012     ,						beta_schedule="scaled_linear"     ,						clip_sample=A_     ,						set_alpha_to_one=A_     ,						)
      torch.manual_seed(0       )
      _lowerCAmelCase   :					Dict        =    AutoencoderKL(
          block_out_channels=[3_2, 6_4]     ,						in_channels=3     ,						out_channels=3     ,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,						latent_channels=4     ,						sample_size=1_2_8     ,						)
      torch.manual_seed(0       )
      _lowerCAmelCase   :					Union[str, Any]        =    CLIPTextConfig(
          bos_token_id=0     ,						eos_token_id=2     ,						hidden_size=3_2     ,						intermediate_size=3_7     ,						layer_norm_eps=1E-05     ,						num_attention_heads=4     ,						num_hidden_layers=5     ,						pad_token_id=1     ,						vocab_size=1_0_0_0     ,						hidden_act="gelu"     ,						projection_dim=5_1_2     ,						)
      _lowerCAmelCase   :					Any        =    CLIPTextModel(A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

      _lowerCAmelCase   :					Dict        =    {
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
      }
      return components

 def    __magic_name__       (		self :	str     ,						A_ :	Union[str, Any]     ,						A_ :	List[str]=0       ):






      '''simple docstring'''




      if str(A_       ).startswith("mps"       ):
           _lowerCAmelCase   :					Any        =    torch.manual_seed(A_       )
      else:
           _lowerCAmelCase   :					Optional[int]        =    torch.Generator(device=A_       ).manual_seed(A_       )
      _lowerCAmelCase   :					List[str]        =    {
          "prompt": "A painting of a squirrel eating a burger",
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "pt",
      }
      return inputs

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    "cpu"  # ensure determinism for the device-dependent torch.Generator
      _lowerCAmelCase   :					Dict        =    self.get_dummy_components()
      _lowerCAmelCase   :					int        =    TextToVideoSDPipeline(**A_       )
      _lowerCAmelCase   :					Dict        =    sd_pipe.to(A_       )
      sd_pipe.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					Optional[Any]        =    self.get_dummy_inputs(A_       )
      _lowerCAmelCase   :					Any        =    "np"
      _lowerCAmelCase   :					List[str]        =    sd_pipe(**A_       ).frames
      _lowerCAmelCase   :					Optional[int]        =    frames[0][-3:, -3:, -1]

      assert frames[0].shape == (6_4, 6_4, 3)
      _lowerCAmelCase   :					str        =    np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]       )

      assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A_     ,						expected_max_diff=3E-3       )

 @unittest.skipIf(
     torch_device != "cuda" or not is_xformers_available()     ,						reason="XFormers attention is only available with CUDA and `xformers` installed"     ,						)
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A_     ,						expected_max_diff=1E-2       )

 @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."       )
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      pass

 @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."       )
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      pass

 @unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline."       )
 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      pass


 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      return super().test_progress_bar()





@slow
@skip_mps
class A__    ( unittest.TestCase ):



 """simple docstring"""







 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"       )

      _lowerCAmelCase   :					Dict        =    TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b"       )
      _lowerCAmelCase   :					Optional[Any]        =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
      _lowerCAmelCase   :					Optional[Any]        =    pipe.to("cuda"       )

      _lowerCAmelCase   :					Union[str, Any]        =    "Spiderman is surfing"
      _lowerCAmelCase   :					Any        =    torch.Generator(device="cpu"       ).manual_seed(0       )

      _lowerCAmelCase   :					Union[str, Any]        =    pipe(A_     ,						generator=A_     ,						num_inference_steps=2_5     ,						output_type="pt"       ).frames
      _lowerCAmelCase   :					Optional[Any]        =    video_frames.cpu().numpy()

      assert np.abs(expected_video - video       ).mean() < 5E-2


 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"       )

      _lowerCAmelCase   :					Optional[Any]        =    TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b"       )
      _lowerCAmelCase   :					Optional[int]        =    pipe.to("cuda"       )

      _lowerCAmelCase   :					List[Any]        =    "Spiderman is surfing"
      _lowerCAmelCase   :					List[str]        =    torch.Generator(device="cpu"       ).manual_seed(0       )

      _lowerCAmelCase   :					List[Any]        =    pipe(A_     ,						generator=A_     ,						num_inference_steps=2     ,						output_type="pt"       ).frames
      _lowerCAmelCase   :					str        =    video_frames.cpu().numpy()

      assert np.abs(expected_video - video       ).mean() < 5E-2









import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
  import torch

  from transformers import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
  )





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Tuple     ,						A_ :	Optional[Any]     ,						A_ :	Dict=1_3     ,						A_ :	str=7     ,						A_ :	Union[str, Any]=True     ,						A_ :	int=True     ,						A_ :	Any=False     ,						A_ :	str=True     ,						A_ :	int=9_9     ,						A_ :	int=3_2     ,						A_ :	Optional[int]=5     ,						A_ :	List[str]=4     ,						A_ :	int=6_4     ,						A_ :	Optional[int]="gelu"     ,						A_ :	List[Any]=0.1     ,						A_ :	int=0.1     ,						A_ :	List[str]=5_1_2     ,						A_ :	Optional[Any]=1_6     ,						A_ :	int=2     ,						A_ :	Optional[int]=0.02     ,						A_ :	Any=3     ,						A_ :	Optional[Any]=4     ,						A_ :	Union[str, Any]=None     ,						A_ :	Union[str, Any]=2     ,						A_ :	Tuple=2     ,						A_ :	Optional[int]=2     ,						A_ :	List[Any]=2     ,						A_ :	List[str]=4     ,						A_ :	Union[str, Any]=1     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    parent
      _lowerCAmelCase   :					List[str]        =    batch_size
      _lowerCAmelCase   :					str        =    seq_length
      _lowerCAmelCase   :					int        =    is_training
      _lowerCAmelCase   :					Union[str, Any]        =    use_input_mask
      _lowerCAmelCase   :					Union[str, Any]        =    use_token_type_ids
      _lowerCAmelCase   :					List[Any]        =    use_labels
      _lowerCAmelCase   :					Optional[int]        =    vocab_size
      _lowerCAmelCase   :					Union[str, Any]        =    hidden_size
      _lowerCAmelCase   :					int        =    num_hidden_layers
      _lowerCAmelCase   :					Optional[int]        =    num_attention_heads
      _lowerCAmelCase   :					Optional[Any]        =    intermediate_size
      _lowerCAmelCase   :					Any        =    hidden_act
      _lowerCAmelCase   :					Tuple        =    hidden_dropout_prob
      _lowerCAmelCase   :					Optional[int]        =    attention_probs_dropout_prob
      _lowerCAmelCase   :					List[Any]        =    max_position_embeddings
      _lowerCAmelCase   :					List[Any]        =    type_vocab_size
      _lowerCAmelCase   :					int        =    type_sequence_label_size
      _lowerCAmelCase   :					List[str]        =    initializer_range
      _lowerCAmelCase   :					Tuple        =    num_labels
      _lowerCAmelCase   :					Dict        =    num_choices
      _lowerCAmelCase   :					Optional[Any]        =    scope
      _lowerCAmelCase   :					Union[str, Any]        =    q_groups
      _lowerCAmelCase   :					Tuple        =    k_groups
      _lowerCAmelCase   :					str        =    v_groups
      _lowerCAmelCase   :					Tuple        =    post_attention_groups
      _lowerCAmelCase   :					Tuple        =    intermediate_groups
      _lowerCAmelCase   :					List[Any]        =    output_groups

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size       )

      _lowerCAmelCase   :					int        =    None
      if self.use_input_mask:
           _lowerCAmelCase   :					Union[str, Any]        =    random_attention_mask([self.batch_size, self.seq_length]       )

      _lowerCAmelCase   :					Dict        =    None
      _lowerCAmelCase   :					Any        =    None
      _lowerCAmelCase   :					List[Any]        =    None
      if self.use_labels:
           _lowerCAmelCase   :					List[Any]        =    ids_tensor([self.batch_size]     ,						self.type_sequence_label_size       )
           _lowerCAmelCase   :					Any        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.num_labels       )
           _lowerCAmelCase   :					Tuple        =    ids_tensor([self.batch_size]     ,						self.num_choices       )

      _lowerCAmelCase   :					Any        =    self.get_config()

      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return SqueezeBertConfig(
          embedding_size=self.hidden_size     ,						vocab_size=self.vocab_size     ,						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     ,						attention_probs_dropout_prob=self.hidden_dropout_prob     ,						attention_dropout=self.attention_probs_dropout_prob     ,						max_position_embeddings=self.max_position_embeddings     ,						initializer_range=self.initializer_range     ,						q_groups=self.q_groups     ,						k_groups=self.k_groups     ,						v_groups=self.v_groups     ,						post_attention_groups=self.post_attention_groups     ,						intermediate_groups=self.intermediate_groups     ,						output_groups=self.output_groups     ,						)

 def    __magic_name__       (		self :	List[str]     ,						A_ :	Dict     ,						A_ :	Union[str, Any]     ,						A_ :	List[str]     ,						A_ :	Optional[int]     ,						A_ :	str     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertModel(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[Any]        =    model(A_     ,						A_       )
      _lowerCAmelCase   :					Tuple        =    model(A_       )
      self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	int     ,						A_ :	Dict     ,						A_ :	Any     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertForMaskedLM(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					int        =    model(A_     ,						attention_mask=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size)       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Union[str, Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	List[str]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertForQuestionAnswering(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[Any]        =    model(
          A_     ,						attention_mask=A_     ,						start_positions=A_     ,						end_positions=A_       )
      self.parent.assertEqual(result.start_logits.shape     ,						(self.batch_size, self.seq_length)       )
      self.parent.assertEqual(result.end_logits.shape     ,						(self.batch_size, self.seq_length)       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	Optional[int]     ,						A_ :	Dict     ,						A_ :	str     ,						A_ :	Tuple     ,						A_ :	List[Any]     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.num_labels
      _lowerCAmelCase   :					int        =    SqueezeBertForSequenceClassification(A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[Any]        =    model(A_     ,						attention_mask=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels)       )

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	Tuple     ,						A_ :	List[Any]     ,						A_ :	List[Any]     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.num_labels
      _lowerCAmelCase   :					Any        =    SqueezeBertForTokenClassification(config=A_       )
      model.to(A_       )
      model.eval()

      _lowerCAmelCase   :					Any        =    model(A_     ,						attention_mask=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.num_labels)       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	Tuple     ,						A_ :	Tuple     ,						A_ :	Union[str, Any]     ,						A_ :	int     ,						A_ :	List[Any]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.num_choices
      _lowerCAmelCase   :					Dict        =    SqueezeBertForMultipleChoice(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[str]        =    input_ids.unsqueeze(1       ).expand(-1     ,						self.num_choices     ,						-1       ).contiguous()
      _lowerCAmelCase   :					Dict        =    input_mask.unsqueeze(1       ).expand(-1     ,						self.num_choices     ,						-1       ).contiguous()
      _lowerCAmelCase   :					str        =    model(
          A_     ,						attention_mask=A_     ,						labels=A_     ,						)
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_choices)       )


 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.prepare_config_and_inputs()
      ((_lowerCAmelCase)      ,			(_lowerCAmelCase)      ,			(_lowerCAmelCase)      ,			(_lowerCAmelCase)      ,			(_lowerCAmelCase)      ,			(_lowerCAmelCase))   :					Optional[int]        =    config_and_inputs
      _lowerCAmelCase   :					Optional[Any]        =    {"input_ids": input_ids, "attention_mask": input_mask}
      return config, inputs_dict





@require_torch
class A__    ( A  ,		A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Tuple      =						(
     (
         SqueezeBertModel,
         SqueezeBertForMaskedLM,
         SqueezeBertForMultipleChoice,
         SqueezeBertForQuestionAnswering,
         SqueezeBertForSequenceClassification,
         SqueezeBertForTokenClassification,
     )
     if is_torch_available()
     else None
 )
 _lowercase     :					Optional[Any]      =						(
     {
         '''feature-extraction''': SqueezeBertModel,
         '''fill-mask''': SqueezeBertForMaskedLM,
         '''question-answering''': SqueezeBertForQuestionAnswering,
         '''text-classification''': SqueezeBertForSequenceClassification,
         '''token-classification''': SqueezeBertForTokenClassification,
         '''zero-shot''': SqueezeBertForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _lowercase     :					Union[str, Any]      =						False
 _lowercase     :					int      =						True
 _lowercase     :					List[str]      =						False

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    SqueezeBertModelTester(self       )
      _lowerCAmelCase   :					Tuple        =    ConfigTester(self     ,						config_class=A_     ,						dim=3_7       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      self.config_tester.run_common_tests()

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_model(*A_       )

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_masked_lm(*A_       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_question_answering(*A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_sequence_classification(*A_       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_token_classification(*A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_multiple_choice(*A_       )


 @slow
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowerCAmelCase   :					int        =    SqueezeBertModel.from_pretrained(A_       )
           self.assertIsNotNone(A_       )





@require_sentencepiece
@require_tokenizers
@require_torch
class A__    ( unittest.TestCase ):



 """simple docstring"""







 @slow
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"       )

      _lowerCAmelCase   :					Optional[int]        =    torch.tensor([[1, 2_9_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 1_3, 1_5_8_8, 2]]       )
      _lowerCAmelCase   :					List[str]        =    model(A_       )[0]
      _lowerCAmelCase   :					Any        =    torch.Size((1, 3)       )
      self.assertEqual(output.shape     ,						A_       )
      _lowerCAmelCase   :					Any        =    torch.tensor([[0.6401, -0.0349, -0.6041]]       )
      self.assertTrue(torch.allclose(A_     ,						A_     ,						atol=1E-4       )       )






