

from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_a	 =     logging.get_logger(__name__)  # pylint: disable=invalid-name


_a	 =     '''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''






class 							A_				(	snake_case__      ):


     def __init__(							self    :	Any    ,						UpperCAmelCase    :	UNetaDModel    ,						UpperCAmelCase    :	CMStochasticIterativeScheduler      )							->			None:
      super().__init__()

      self.register_modules(
          unet=UpperCAmelCase    ,						scheduler=UpperCAmelCase    ,						)

      __lowerCAmelCase:     Any     			=   None


     def 						UpperCAmelCase						(							self    :	List[str]    ,						UpperCAmelCase    :	int=0      )							->			Union[str, Any]:
      if is_accelerate_available() and is_accelerate_version('>='    ,						'0.14.0'      ):
       from accelerate import cpu_offload
      else:
       raise ImportError('`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'      )

      __lowerCAmelCase:     Dict     			=   torch.device(F'''cuda:{gpu_id}'''      )

      if self.device.type != "cpu":
       self.to('cpu'    ,						silence_dtype_warnings=UpperCAmelCase      )
       torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

      for cpu_offloaded_model in [self.unet]:
       cpu_offload(UpperCAmelCase    ,						UpperCAmelCase      )

      if self.safety_checker is not None:
       cpu_offload(self.safety_checker    ,						execution_device=UpperCAmelCase    ,						offload_buffers=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Dict    ,						UpperCAmelCase    :	Any=0      )							->			Any:
      if is_accelerate_available() and is_accelerate_version('>='    ,						'0.17.0.dev0'      ):
       from accelerate import cpu_offload_with_hook
      else:
       raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'      )

      __lowerCAmelCase:     Union[str, Any]     			=   torch.device(F'''cuda:{gpu_id}'''      )

      if self.device.type != "cpu":
       self.to('cpu'    ,						silence_dtype_warnings=UpperCAmelCase      )
       torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

      __lowerCAmelCase:     Optional[int]     			=   None
      for cpu_offloaded_model in [self.unet]:
       __lowerCAmelCase			,							__lowerCAmelCase:     Tuple     			=   cpu_offload_with_hook(UpperCAmelCase    ,						UpperCAmelCase    ,						prev_module_hook=UpperCAmelCase      )

      if self.safety_checker is not None:
       __lowerCAmelCase			,							__lowerCAmelCase:     int     			=   cpu_offload_with_hook(self.safety_checker    ,						UpperCAmelCase    ,						prev_module_hook=UpperCAmelCase      )

      # We'll offload the last model manually.
      __lowerCAmelCase:     int     			=   hook


     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 						UpperCAmelCase						(							self    :	Optional[Any]      )							->			List[str]:
      if not hasattr(self.unet    ,						'_hf_hook'      ):
       return self.device
      for module in self.unet.modules():
       if (
           hasattr(UpperCAmelCase    ,						'_hf_hook'      )
           and hasattr(module._hf_hook    ,						'execution_device'      )
           and module._hf_hook.execution_device is not None
       ):
        return torch.device(module._hf_hook.execution_device      )
      return self.device


     def 						UpperCAmelCase						(							self    :	int    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	Tuple    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	Union[str, Any]    ,						UpperCAmelCase    :	List[Any]    ,						UpperCAmelCase    :	Optional[Any]    ,						UpperCAmelCase    :	List[Any]    ,						UpperCAmelCase    :	List[str]=None      )							->			Dict:
      __lowerCAmelCase:     List[str]     			=   (batch_size, num_channels, height, width)
      if isinstance(UpperCAmelCase    ,						UpperCAmelCase      ) and len(UpperCAmelCase      ) != batch_size:
       raise ValueError(
           F'''You have passed a list of generators of length {len(UpperCAmelCase      )}, but requested an effective batch'''
           F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

      if latents is None:
       __lowerCAmelCase:     Optional[int]     			=   randn_tensor(UpperCAmelCase    ,						generator=UpperCAmelCase    ,						device=UpperCAmelCase    ,						dtype=UpperCAmelCase      )
      else:
       __lowerCAmelCase:     Dict     			=   latents.to(device=UpperCAmelCase    ,						dtype=UpperCAmelCase      )

      # scale the initial noise by the standard deviation required by the scheduler
      __lowerCAmelCase:     Dict     			=   latents * self.scheduler.init_noise_sigma
      return latents


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	torch.FloatTensor    ,						UpperCAmelCase    :	str = "pil"      )							->			Dict:
      if output_type not in ["pt", "np", "pil"]:
       raise ValueError(
           F'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''      )

      # Equivalent to diffusers.VaeImageProcessor.denormalize
      __lowerCAmelCase:     Union[str, Any]     			=   (sample / 2 + 0.5).clamp(0    ,						1      )
      if output_type == "pt":
       return sample

      # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
      __lowerCAmelCase:     List[Any]     			=   sample.cpu().permute(0    ,						2    ,						3    ,						1      ).numpy()
      if output_type == "np":
       return sample

      # Output_type must be 'pil'
      __lowerCAmelCase:     List[Any]     			=   self.numpy_to_pil(UpperCAmelCase      )
      return sample


     def 						UpperCAmelCase						(							self    :	Optional[int]    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	List[Any]=None      )							->			int:
      if self.unet.config.num_class_embeds is not None:
       if isinstance(UpperCAmelCase    ,						UpperCAmelCase      ):
        __lowerCAmelCase:     Optional[int]     			=   torch.tensor(UpperCAmelCase    ,						dtype=torch.int      )
       elif isinstance(UpperCAmelCase    ,						UpperCAmelCase      ):
        assert batch_size == 1, "Batch size must be 1 if classes is an int"
        __lowerCAmelCase:     Optional[int]     			=   torch.tensor([class_labels]    ,						dtype=torch.int      )
       elif class_labels is None:
        # Randomly generate batch_size class labels
        # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
        __lowerCAmelCase:     Optional[int]     			=   torch.randint(0    ,						self.unet.config.num_class_embeds    ,						size=(batch_size,)      )
       __lowerCAmelCase:     List[Any]     			=   class_labels.to(UpperCAmelCase      )
      else:
       __lowerCAmelCase:     Dict     			=   None
      return class_labels


     def 						UpperCAmelCase						(							self    :	List[str]    ,						UpperCAmelCase    :	Dict    ,						UpperCAmelCase    :	Union[str, Any]    ,						UpperCAmelCase    :	List[Any]    ,						UpperCAmelCase    :	Dict    ,						UpperCAmelCase    :	Tuple    ,						UpperCAmelCase    :	List[str]      )							->			Any:
      if num_inference_steps is None and timesteps is None:
       raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'      )

      if num_inference_steps is not None and timesteps is not None:
       logger.warning(
           F'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
           ' `timesteps` will be used over `num_inference_steps`.'      )

      if latents is not None:
       __lowerCAmelCase:     str     			=   (batch_size, 3, img_size, img_size)
       if latents.shape != expected_shape:
        raise ValueError(F'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''      )

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


     @torch.no_grad()
     @replace_example_docstring(UpperCAmelCase      )
     def __call__(							self    :	List[str]    ,						UpperCAmelCase    :	int = 1    ,						UpperCAmelCase    :	Optional[Union[torch.Tensor, List[int], int]] = None    ,						UpperCAmelCase    :	int = 1    ,						UpperCAmelCase    :	List[int] = None    ,						UpperCAmelCase    :	Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,						UpperCAmelCase    :	Optional[torch.FloatTensor] = None    ,						UpperCAmelCase    :	Optional[str] = "pil"    ,						UpperCAmelCase    :	bool = True    ,						UpperCAmelCase    :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,						UpperCAmelCase    :	int = 1    ,						)							->			Optional[Any]:
      __lowerCAmelCase:     Optional[int]     			=   self.unet.config.sample_size
      __lowerCAmelCase:     int     			=   self._execution_device

      # 1. Check inputs
      self.check_inputs(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )

      # 2. Prepare image latents
      # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
      __lowerCAmelCase:     Any     			=   self.prepare_latents(
          batch_size=UpperCAmelCase    ,						num_channels=self.unet.config.in_channels    ,						height=UpperCAmelCase    ,						width=UpperCAmelCase    ,						dtype=self.unet.dtype    ,						device=UpperCAmelCase    ,						generator=UpperCAmelCase    ,						latents=UpperCAmelCase    ,						)

      # 3. Handle class_labels for class-conditional models
      __lowerCAmelCase:     str     			=   self.prepare_class_labels(UpperCAmelCase    ,						UpperCAmelCase    ,						class_labels=UpperCAmelCase      )

      # 4. Prepare timesteps
      if timesteps is not None:
       self.scheduler.set_timesteps(timesteps=UpperCAmelCase    ,						device=UpperCAmelCase      )
       __lowerCAmelCase:     Optional[Any]     			=   self.scheduler.timesteps
       __lowerCAmelCase:     Union[str, Any]     			=   len(UpperCAmelCase      )
      else:
       self.scheduler.set_timesteps(UpperCAmelCase      )
       __lowerCAmelCase:     List[Any]     			=   self.scheduler.timesteps

      # 5. Denoising loop
      # Multistep sampling: implements Algorithm 1 in the paper
      with self.progress_bar(total=UpperCAmelCase      ) as progress_bar:
       for i, t in enumerate(UpperCAmelCase      ):
        __lowerCAmelCase:     Optional[int]     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						UpperCAmelCase      )
        __lowerCAmelCase:     str     			=   self.unet(UpperCAmelCase    ,						UpperCAmelCase    ,						class_labels=UpperCAmelCase    ,						return_dict=UpperCAmelCase      )[0]

        __lowerCAmelCase:     Any     			=   self.scheduler.step(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						generator=UpperCAmelCase      )[0]

        # call the callback, if provided
        progress_bar.update()
        if callback is not None and i % callback_steps == 0:
         callback(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )

        # 6. Post-process image sample
      __lowerCAmelCase:     Dict     			=   self.postprocess_image(UpperCAmelCase    ,						output_type=UpperCAmelCase      )

      # Offload last model to CPU
      if hasattr(self    ,						'final_offload_hook'      ) and self.final_offload_hook is not None:
       self.final_offload_hook.offload()

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase      )








from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel






@require_tf
class 							A_				:
     _lowercase    :  str          =			MarianConfig
     _lowercase    :  Optional[int]          =			{}
     _lowercase    :  Optional[Any]          =			'gelu'


     def __init__(							self    :	Dict    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	List[Any]=1_3    ,						UpperCAmelCase    :	Optional[int]=7    ,						UpperCAmelCase    :	List[str]=True    ,						UpperCAmelCase    :	Dict=False    ,						UpperCAmelCase    :	Union[str, Any]=9_9    ,						UpperCAmelCase    :	Tuple=3_2    ,						UpperCAmelCase    :	List[str]=2    ,						UpperCAmelCase    :	Dict=4    ,						UpperCAmelCase    :	Tuple=3_7    ,						UpperCAmelCase    :	Union[str, Any]=0.1    ,						UpperCAmelCase    :	Dict=0.1    ,						UpperCAmelCase    :	Any=2_0    ,						UpperCAmelCase    :	Union[str, Any]=2    ,						UpperCAmelCase    :	Any=1    ,						UpperCAmelCase    :	str=0    ,						)							->			str:
      __lowerCAmelCase:     Tuple     			=   parent
      __lowerCAmelCase:     Any     			=   batch_size
      __lowerCAmelCase:     List[Any]     			=   seq_length
      __lowerCAmelCase:     Dict     			=   is_training
      __lowerCAmelCase:     List[Any]     			=   use_labels
      __lowerCAmelCase:     List[str]     			=   vocab_size
      __lowerCAmelCase:     Optional[Any]     			=   hidden_size
      __lowerCAmelCase:     Any     			=   num_hidden_layers
      __lowerCAmelCase:     Optional[Any]     			=   num_attention_heads
      __lowerCAmelCase:     int     			=   intermediate_size

      __lowerCAmelCase:     List[Any]     			=   hidden_dropout_prob
      __lowerCAmelCase:     List[str]     			=   attention_probs_dropout_prob
      __lowerCAmelCase:     Tuple     			=   max_position_embeddings
      __lowerCAmelCase:     Tuple     			=   eos_token_id
      __lowerCAmelCase:     List[Any]     			=   pad_token_id
      __lowerCAmelCase:     str     			=   bos_token_id


     def 						UpperCAmelCase						(							self    :	str      )							->			Optional[int]:
      __lowerCAmelCase:     int     			=   ids_tensor([self.batch_size, self.seq_length - 1]    ,						self.vocab_size      )
      __lowerCAmelCase:     Optional[int]     			=   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )    ,						1      )
      __lowerCAmelCase:     List[Any]     			=   tf.concat([input_ids, eos_tensor]    ,						axis=1      )

      __lowerCAmelCase:     str     			=   ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size      )

      __lowerCAmelCase:     Optional[int]     			=   self.config_cls(
          vocab_size=self.vocab_size    ,						d_model=self.hidden_size    ,						encoder_layers=self.num_hidden_layers    ,						decoder_layers=self.num_hidden_layers    ,						encoder_attention_heads=self.num_attention_heads    ,						decoder_attention_heads=self.num_attention_heads    ,						encoder_ffn_dim=self.intermediate_size    ,						decoder_ffn_dim=self.intermediate_size    ,						dropout=self.hidden_dropout_prob    ,						attention_dropout=self.attention_probs_dropout_prob    ,						max_position_embeddings=self.max_position_embeddings    ,						eos_token_ids=[2]    ,						bos_token_id=self.bos_token_id    ,						pad_token_id=self.pad_token_id    ,						decoder_start_token_id=self.pad_token_id    ,						**self.config_updates    ,						)
      __lowerCAmelCase:     Any     			=   prepare_marian_inputs_dict(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )
      return config, inputs_dict


     def 						UpperCAmelCase						(							self    :	int    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	int      )							->			Union[str, Any]:
      __lowerCAmelCase:     List[Any]     			=   TFMarianModel(config=UpperCAmelCase      ).get_decoder()
      __lowerCAmelCase:     str     			=   inputs_dict['input_ids']

      __lowerCAmelCase:     Tuple     			=   input_ids[:1, :]
      __lowerCAmelCase:     Optional[Any]     			=   inputs_dict['attention_mask'][:1, :]
      __lowerCAmelCase:     Dict     			=   inputs_dict['head_mask']
      __lowerCAmelCase:     List[Any]     			=   1

      # first forward pass
      __lowerCAmelCase:     Union[str, Any]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase    ,						head_mask=UpperCAmelCase    ,						use_cache=UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     int     			=   outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      __lowerCAmelCase:     int     			=   ids_tensor((self.batch_size, 3)    ,						config.vocab_size      )
      __lowerCAmelCase:     Tuple     			=   tf.cast(ids_tensor((self.batch_size, 3)    ,						2      )    ,						tf.inta      )

      # append to next input_ids and
      __lowerCAmelCase:     Any     			=   tf.concat([input_ids, next_tokens]    ,						axis=-1      )
      __lowerCAmelCase:     Optional[int]     			=   tf.concat([attention_mask, next_attn_mask]    ,						axis=-1      )

      __lowerCAmelCase:     Optional[int]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase      )[0]
      __lowerCAmelCase:     List[str]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase    ,						past_key_values=UpperCAmelCase      )[0]

      self.parent.assertEqual(next_tokens.shape[1]    ,						output_from_past.shape[1]      )

      # select random slice
      __lowerCAmelCase:     Optional[int]     			=   int(ids_tensor((1,)    ,						output_from_past.shape[-1]      )      )
      __lowerCAmelCase:     Tuple     			=   output_from_no_past[:, -3:, random_slice_idx]
      __lowerCAmelCase:     Dict     			=   output_from_past[:, :, random_slice_idx]

      # test that outputs are equal for slice
      tf.debugging.assert_near(UpperCAmelCase    ,						UpperCAmelCase    ,						rtol=1E-3      )







def _a				( SCREAMING_SNAKE_CASE	:    str					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Optional[int]=None					,      SCREAMING_SNAKE_CASE	:    Dict=None					,      SCREAMING_SNAKE_CASE	:    str=None					,      SCREAMING_SNAKE_CASE	:    List[str]=None					,      SCREAMING_SNAKE_CASE	:    str=None					,      )					->					Dict:

 """simple docstring"""






 if attention_mask is None:
  __lowerCAmelCase:     Dict     			=   tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE					,      config.pad_token_id	)					,      tf.inta	)
 if decoder_attention_mask is None:
  __lowerCAmelCase:     Tuple     			=   tf.concat(
      [
          tf.ones(decoder_input_ids[:, :1].shape					,      dtype=tf.inta	),
          tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]					,      config.pad_token_id	)					,      tf.inta	),
      ]					,      axis=-1					,      )
 if head_mask is None:
  __lowerCAmelCase:     List[str]     			=   tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
 if decoder_head_mask is None:
  __lowerCAmelCase:     List[Any]     			=   tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
 if cross_attn_head_mask is None:
  __lowerCAmelCase:     Dict     			=   tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }






@require_tf
class 							A_				(	snake_case__							,			snake_case__							,			unittest.TestCase      ):
     _lowercase    :  Optional[int]          =			(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     _lowercase    :  int          =			(TFMarianMTModel,) if is_tf_available() else ()
     _lowercase    :  Optional[int]          =			(
         {
             'conversational': TFMarianMTModel,
             'feature-extraction': TFMarianModel,
             'summarization': TFMarianMTModel,
             'text2text-generation': TFMarianMTModel,
             'translation': TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     _lowercase    :  Dict          =			True
     _lowercase    :  List[Any]          =			False
     _lowercase    :  Optional[int]          =			False


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Any:
      __lowerCAmelCase:     List[Any]     			=   TFMarianModelTester(self      )
      __lowerCAmelCase:     Any     			=   ConfigTester(self    ,						config_class=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			Any:
      self.config_tester.run_common_tests()


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Any:
      __lowerCAmelCase:     Tuple     			=   self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase      )







@require_tf
class 							A_				(	unittest.TestCase      ):
     _lowercase    :  int          =			1_0_0_0  # show more chars for failing integration tests


     @classmethod
     def 						UpperCAmelCase						(							cls    :	Dict      )							->			None:
      __lowerCAmelCase:     int     			=   F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
      return cls


     @cached_property
     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			MarianTokenizer:
      return AutoTokenizer.from_pretrained(self.model_name      )


     @property
     def 						UpperCAmelCase						(							self    :	Any      )							->			int:
      return self.tokenizer.eos_token_id


     @cached_property
     def 						UpperCAmelCase						(							self    :	int      )							->			Optional[int]:
      warnings.simplefilter('error'      )
      __lowerCAmelCase:     TFMarianMTModel     			=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
      assert isinstance(UpperCAmelCase    ,						UpperCAmelCase      )
      __lowerCAmelCase:     List[str]     			=   model.config
      self.assertListEqual(c.bad_words_ids    ,						[[c.pad_token_id]]      )
      self.assertEqual(c.max_length    ,						5_1_2      )
      self.assertEqual(c.decoder_start_token_id    ,						c.pad_token_id      )
      return model


     def 						UpperCAmelCase						(							self    :	List[str]    ,						**UpperCAmelCase    :	Dict      )							->			int:
      __lowerCAmelCase:     Optional[int]     			=   self.translate_src_text(**UpperCAmelCase      )
      self.assertListEqual(self.expected_text    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	List[str]    ,						**UpperCAmelCase    :	Optional[int]      )							->			Optional[int]:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(self.src_text    ,						**UpperCAmelCase    ,						padding=UpperCAmelCase    ,						return_tensors='tf'      )
      __lowerCAmelCase:     Tuple     			=   self.model.generate(
          model_inputs.input_ids    ,						attention_mask=model_inputs.attention_mask    ,						num_beams=2    ,						max_length=1_2_8      )
      __lowerCAmelCase:     Union[str, Any]     			=   self.tokenizer.batch_decode(generated_ids.numpy()    ,						skip_special_tokens=UpperCAmelCase      )
      return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  int          =			'mt'
     _lowercase    :  Union[str, Any]          =			'en'
     _lowercase    :  str          =			['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
     _lowercase    :  str          =			['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Any      )							->			List[str]:
      self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  Optional[int]          =			'en'
     _lowercase    :  Optional[Any]          =			'zh'
     _lowercase    :  Tuple          =			['My name is Wolfgang and I live in Berlin']
     _lowercase    :  List[Any]          =			['我叫沃尔夫冈 我住在柏林']


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	List[str]      )							->			Optional[Any]:
      self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  Tuple          =			'en'
     _lowercase    :  Optional[int]          =			'ROMANCE'
     _lowercase    :  Any          =			[
         '>>fr<< Don\'t spend so much time watching TV.',
         '>>pt<< Your message has been sent.',
         '>>es<< He\'s two years older than me.',
     ]
     _lowercase    :  Tuple          =			[
         'Ne passez pas autant de temps à regarder la télé.',
         'A sua mensagem foi enviada.',
         'Es dos años más viejo que yo.',
     ]


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Tuple      )							->			Dict:
      self._assert_generated_batch_equal_expected()


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			List[str]:
      __lowerCAmelCase:     Optional[Any]     			=   TranslationPipeline(self.model    ,						self.tokenizer    ,						framework='tf'      )
      __lowerCAmelCase:     Tuple     			=   pipeline(self.src_text      )
      self.assertEqual(self.expected_text    ,						[x['translation_text'] for x in output]      )





