'''simple docstring'''

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

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


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



def  _lowerCAmelCase				(				self			,      __a			,      __a			,      __a			,      __a      )  ->      str:
     '''simple docstring'''







     _UpperCamelCase  :Dict       =image.to(device=__a      )
     _UpperCamelCase  :Union[str, Any]       =image  # take image as image_embeddings
     _UpperCamelCase  :Dict       =image_embeddings.unsqueeze(1      )

     # duplicate image embeddings for each generation per prompt, using mps friendly method
     _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :str       =image_embeddings.shape
     _UpperCamelCase  :int       =image_embeddings.repeat(1			,      __a			,      1      )
     _UpperCamelCase  :int       =image_embeddings.view(bs_embed * num_images_per_prompt			,      __a			,      -1      )

     if do_classifier_free_guidance:
          _UpperCamelCase  :str       =torch.zeros_like(__a      )

          # 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
          _UpperCamelCase  :List[Any]       =torch.cat([uncond_embeddings, image_embeddings]      )

     return image_embeddings




class    lowerCamelCase__  (   __snake_case     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      )				->     Union[str, Any]:
        """simple docstring"""





        super().__init__()

        _UpperCamelCase  :Any       ={"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

        _UpperCamelCase  :List[Any]       =decoder_pipe_kwargs.get("""torch_dtype"""					,      lowerCAmelCase__     ) or prior.dtype

        _UpperCamelCase  :Union[str, Any]       =StableDiffusionImageVariationPipeline.from_pretrained(
            """lambdalabs/sd-image-variations-diffusers"""					,      **lowerCAmelCase__     )

        # replace `_encode_image` method
        _UpperCamelCase  :Tuple       =types.MethodType(_encode_image					,      self.decoder_pipe     )

        self.register_modules(
            prior=lowerCAmelCase__					,      tokenizer=lowerCAmelCase__					,      text_encoder=lowerCAmelCase__					,      prior_scheduler=lowerCAmelCase__					,      )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      )				->     str:
        """simple docstring"""





        if text_model_output is None:
             _UpperCamelCase  :Optional[int]       =len(lowerCAmelCase__     ) if isinstance(lowerCAmelCase__					,      lowerCAmelCase__     ) else 1
             # get prompt text embeddings
             _UpperCamelCase  :Any       =self.tokenizer(
                 lowerCAmelCase__					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      return_tensors="""pt"""					,      )
             _UpperCamelCase  :List[str]       =text_inputs.input_ids
             _UpperCamelCase  :Tuple       =text_inputs.attention_mask.bool().to(lowerCAmelCase__     )

             if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                  _UpperCamelCase  :Any       =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}'''     )
                  _UpperCamelCase  :Tuple       =text_input_ids[:, : self.tokenizer.model_max_length]

             _UpperCamelCase  :str       =self.text_encoder(text_input_ids.to(lowerCAmelCase__     )     )

             _UpperCamelCase  :str       =text_encoder_output.text_embeds
             _UpperCamelCase  :Any       =text_encoder_output.last_hidden_state

        else:
             _UpperCamelCase  :Dict       =text_model_output[0].shape[0]
             _UpperCamelCase       ,      _UpperCamelCase  :Dict       =text_model_output[0], text_model_output[1]
             _UpperCamelCase  :Tuple       =text_attention_mask

        _UpperCamelCase  :Optional[Any]       =text_embeddings.repeat_interleave(lowerCAmelCase__					,      dim=0     )
        _UpperCamelCase  :Optional[int]       =text_encoder_hidden_states.repeat_interleave(lowerCAmelCase__					,      dim=0     )
        _UpperCamelCase  :List[Any]       =text_mask.repeat_interleave(lowerCAmelCase__					,      dim=0     )

        if do_classifier_free_guidance:
             _UpperCamelCase  :Optional[int]       =[""""""] * batch_size

             _UpperCamelCase  :Dict       =self.tokenizer(
                 lowerCAmelCase__					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      truncation=lowerCAmelCase__					,      return_tensors="""pt"""					,      )
             _UpperCamelCase  :Any       =uncond_input.attention_mask.bool().to(lowerCAmelCase__     )
             _UpperCamelCase  :Union[str, Any]       =self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase__     )     )

             _UpperCamelCase  :Optional[int]       =uncond_embeddings_text_encoder_output.text_embeds
             _UpperCamelCase  :Optional[Any]       =uncond_embeddings_text_encoder_output.last_hidden_state

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

             _UpperCamelCase  :List[Any]       =uncond_embeddings.shape[1]
             _UpperCamelCase  :Dict       =uncond_embeddings.repeat(1					,      lowerCAmelCase__     )
             _UpperCamelCase  :Optional[int]       =uncond_embeddings.view(batch_size * num_images_per_prompt					,      lowerCAmelCase__     )

             _UpperCamelCase  :Optional[Any]       =uncond_text_encoder_hidden_states.shape[1]
             _UpperCamelCase  :List[Any]       =uncond_text_encoder_hidden_states.repeat(1					,      lowerCAmelCase__					,      1     )
             _UpperCamelCase  :Optional[Any]       =uncond_text_encoder_hidden_states.view(
                 batch_size * num_images_per_prompt					,      lowerCAmelCase__					,      -1     )
             _UpperCamelCase  :Tuple       =uncond_text_mask.repeat_interleave(lowerCAmelCase__					,      dim=0     )

             # done duplicates

             # 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
             _UpperCamelCase  :Any       =torch.cat([uncond_embeddings, text_embeddings]     )
             _UpperCamelCase  :Any       =torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

             _UpperCamelCase  :Dict       =torch.cat([uncond_text_mask, text_mask]     )

        return text_embeddings, text_encoder_hidden_states, text_mask



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        if self.device != torch.device("""meta"""     ) or not hasattr(self.prior					,      """_hf_hook"""     ):
             return self.device
        for module in self.prior.modules():
             if (
                 hasattr(lowerCAmelCase__					,      """_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					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        if latents is None:
             _UpperCamelCase  :Optional[int]       =randn_tensor(lowerCAmelCase__					,      generator=lowerCAmelCase__					,      device=lowerCAmelCase__					,      dtype=lowerCAmelCase__     )
        else:
             if latents.shape != shape:
                  raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''     )
             _UpperCamelCase  :List[str]       =latents.to(lowerCAmelCase__     )

        _UpperCamelCase  :Any       =latents * scheduler.init_noise_sigma
        return latents



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__ = None     )				->     int:
        """simple docstring"""





        self.decoder_pipe.to(lowerCAmelCase__     )
        super().to(lowerCAmelCase__     )




   @torch.no_grad()
   def __call__(    self					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = 1					,      lowerCAmelCase__ = 25					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = 4.0					,      lowerCAmelCase__ = 8.0					,      lowerCAmelCase__ = 50					,      lowerCAmelCase__ = 1					,      lowerCAmelCase__ = 0.0					,      lowerCAmelCase__ = "pil"					,      lowerCAmelCase__ = True					,      )				->     Dict:
        """simple docstring"""





        if prompt is not None:
             if isinstance(lowerCAmelCase__					,      lowerCAmelCase__     ):
                  _UpperCamelCase  :List[Any]       =1
             elif isinstance(lowerCAmelCase__					,      lowerCAmelCase__     ):
                  _UpperCamelCase  :List[Any]       =len(lowerCAmelCase__     )
             else:
                  raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__     )}'''     )
        else:
             _UpperCamelCase  :int       =text_model_output[0].shape[0]

        _UpperCamelCase  :List[str]       =self._execution_device

        _UpperCamelCase  :List[str]       =batch_size * num_images_per_prompt

        _UpperCamelCase  :Optional[Any]       =prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

        _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =self._encode_prompt(
            lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )

        # prior

        self.prior_scheduler.set_timesteps(lowerCAmelCase__					,      device=lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =self.prior_scheduler.timesteps

        _UpperCamelCase  :int       =self.prior.config.embedding_dim

        _UpperCamelCase  :Union[str, Any]       =self.prepare_latents(
            (batch_size, embedding_dim)					,      text_embeddings.dtype					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      self.prior_scheduler					,      )

        for i, t in enumerate(self.progress_bar(lowerCAmelCase__     )     ):
             # expand the latents if we are doing classifier free guidance
             _UpperCamelCase  :Optional[int]       =torch.cat([prior_latents] * 2     ) if do_classifier_free_guidance else prior_latents

             _UpperCamelCase  :Dict       =self.prior(
                 lowerCAmelCase__					,      timestep=lowerCAmelCase__					,      proj_embedding=lowerCAmelCase__					,      encoder_hidden_states=lowerCAmelCase__					,      attention_mask=lowerCAmelCase__					,      ).predicted_image_embedding

             if do_classifier_free_guidance:
                  _UpperCamelCase       ,      _UpperCamelCase  :Tuple       =predicted_image_embedding.chunk(2     )
                  _UpperCamelCase  :Dict       =predicted_image_embedding_uncond + prior_guidance_scale * (
                      predicted_image_embedding_text - predicted_image_embedding_uncond
                  )

             if i + 1 == prior_timesteps_tensor.shape[0]:
                  _UpperCamelCase  :Tuple       =None
             else:
                  _UpperCamelCase  :Optional[int]       =prior_timesteps_tensor[i + 1]

             _UpperCamelCase  :Tuple       =self.prior_scheduler.step(
                 lowerCAmelCase__					,      timestep=lowerCAmelCase__					,      sample=lowerCAmelCase__					,      generator=lowerCAmelCase__					,      prev_timestep=lowerCAmelCase__					,      ).prev_sample

        _UpperCamelCase  :Any       =self.prior.post_process_latents(lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =prior_latents

        _UpperCamelCase  :Tuple       =self.decoder_pipe(
            image=lowerCAmelCase__					,      height=lowerCAmelCase__					,      width=lowerCAmelCase__					,      num_inference_steps=lowerCAmelCase__					,      guidance_scale=lowerCAmelCase__					,      generator=lowerCAmelCase__					,      output_type=lowerCAmelCase__					,      return_dict=lowerCAmelCase__					,      num_images_per_prompt=lowerCAmelCase__					,      eta=lowerCAmelCase__					,      )
        return output



import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import 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 (
     ErnieMForInformationExtraction,
     ErnieMForMultipleChoice,
     ErnieMForQuestionAnswering,
     ErnieMForSequenceClassification,
     ErnieMForTokenClassification,
     ErnieMModel,
 )
 from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST





class     __a     :
 def __init__(  self							,					lowerCAmelCase__							,					lowerCAmelCase__=13							,					lowerCAmelCase__=7							,					lowerCAmelCase__=True							,					lowerCAmelCase__=True							,					lowerCAmelCase__=True							,					lowerCAmelCase__=99							,					lowerCAmelCase__=32							,					lowerCAmelCase__=5							,					lowerCAmelCase__=4							,					lowerCAmelCase__=37							,					lowerCAmelCase__="gelu"							,					lowerCAmelCase__=0.1							,					lowerCAmelCase__=0.1							,					lowerCAmelCase__=512							,					lowerCAmelCase__=16							,					lowerCAmelCase__=2							,					lowerCAmelCase__=0.0_2							,					lowerCAmelCase__=3							,					lowerCAmelCase__=4							,					lowerCAmelCase__=None							,					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Tuple         =  parent
        lowercase__:  Optional[int]         =  batch_size
        lowercase__:  Optional[Any]         =  seq_length
        lowercase__:  Dict         =  is_training
        lowercase__:  List[str]         =  use_input_mask
        lowercase__:  Tuple         =  use_labels
        lowercase__:  List[str]         =  vocab_size
        lowercase__:  Any         =  hidden_size
        lowercase__:  Optional[int]         =  num_hidden_layers
        lowercase__:  str         =  num_attention_heads
        lowercase__:  Any         =  intermediate_size
        lowercase__:  List[str]         =  hidden_act
        lowercase__:  str         =  hidden_dropout_prob
        lowercase__:  Optional[Any]         =  attention_probs_dropout_prob
        lowercase__:  Optional[Any]         =  max_position_embeddings
        lowercase__:  List[str]         =  type_vocab_size
        lowercase__:  List[Any]         =  type_sequence_label_size
        lowercase__:  Any         =  initializer_range
        lowercase__:  str         =  num_labels
        lowercase__:  Tuple         =  num_choices
        lowercase__:  str         =  scope
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  int         =  ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size					)

        lowercase__:  str         =  None
        if self.use_input_mask:
               lowercase__:  Dict         =  random_attention_mask([self.batch_size, self.seq_length]					)

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

        lowercase__:  int         =  self.get_config()

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        lowercase__:  Tuple         =  ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size					)

        lowercase__:  List[str]         =  None
        if self.use_input_mask:
               lowercase__:  Union[str, Any]         =  random_attention_mask([self.batch_size, self.seq_length]					)
        lowercase__:  Any         =  self.get_config()

        return config, input_ids, input_mask
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        return ErnieMConfig(
            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							,					hidden_dropout_prob=self.hidden_dropout_prob							,					attention_probs_dropout_prob=self.attention_probs_dropout_prob							,					max_position_embeddings=self.max_position_embeddings							,					type_vocab_size=self.type_vocab_size							,					initializer_range=self.initializer_range							,					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							str:




        '''simple docstring'''
        lowercase__:  str         =  ErnieMModel(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  List[Any]         =  model(lowerCAmelCase__							,					return_dict=lowerCAmelCase__					)
        self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  ErnieMForQuestionAnswering(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  List[str]         =  model(
            lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					start_positions=lowerCAmelCase__							,					end_positions=lowerCAmelCase__							,					)
        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 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Dict         =  ErnieMForInformationExtraction(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  Any         =  torch.ones_like(lowerCAmelCase__							,					dtype=torch.floataa					)
        lowercase__:  Tuple         =  model(
            lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					start_positions=lowerCAmelCase__							,					end_positions=lowerCAmelCase__							,					)
        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 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  Tuple         =  self.num_labels
        lowercase__:  str         =  ErnieMForSequenceClassification(lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  Optional[int]         =  model(lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					labels=lowerCAmelCase__					)
        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  self.num_labels
        lowercase__:  Dict         =  ErnieMForTokenClassification(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        input_ids.to(lowerCAmelCase__					)
        input_mask.to(lowerCAmelCase__					)
        token_labels.to(lowerCAmelCase__					)

        lowercase__:  Optional[Any]         =  model(lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					labels=lowerCAmelCase__					)

        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.num_labels)					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  Dict         =  self.num_choices
        lowercase__:  Any         =  ErnieMForMultipleChoice(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  Optional[int]         =  input_ids.unsqueeze(1					).expand(-1							,					self.num_choices							,					-1					).contiguous()
        lowercase__:  Any         =  input_mask.unsqueeze(1					).expand(-1							,					self.num_choices							,					-1					).contiguous()
        lowercase__:  Union[str, Any]         =  model(
            lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					labels=lowerCAmelCase__							,					)
        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_choices)					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  int         =  self.prepare_config_and_inputs()
        (
            (
            lowercase__
        )     ,		(
            lowercase__
        )     ,		(
            lowercase__
        )     ,		(
            lowercase__
        )     ,		(
            lowercase__
        )     ,		(
            lowercase__
        )     ,		
        ):  Optional[Any]         =  config_and_inputs
        lowercase__:  int         =  {'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict





@require_torch
class     __a     (       __UpperCamelCase			,			__UpperCamelCase			,			unittest.TestCase ):
 __lowercase					:				List[str]  	=					(
     (
         ErnieMModel,
         ErnieMForMultipleChoice,
         ErnieMForQuestionAnswering,
         ErnieMForSequenceClassification,
         ErnieMForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __lowercase					:				List[str]  	=					()
 __lowercase					:				Optional[int]  	=					(
     {
         'feature-extraction': ErnieMModel,
         'question-answering': ErnieMForQuestionAnswering,
         'text-classification': ErnieMForSequenceClassification,
         'token-classification': ErnieMForTokenClassification,
         'zero-shot': ErnieMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowercase					:				Optional[Any]  	=					False
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							int:




        '''simple docstring'''
        if pipeline_test_casse_name == "QAPipelineTests":
               return True

        return False
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Any         =  ErnieMModelTester(self					)
        lowercase__:  List[str]         =  ConfigTester(self							,					config_class=lowerCAmelCase__							,					hidden_size=37					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        self.config_tester.run_common_tests()
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  int         =  self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
               lowercase__:  str         =  type
               self.model_tester.create_and_check_model(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        lowercase__:  Tuple         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Any         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Optional[int]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_information_extraction(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Optional[int]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__					)
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Any:




        '''simple docstring'''
        for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__:  Tuple         =  ErnieMModel.from_pretrained(lowerCAmelCase__					)
               self.assertIsNotNone(lowerCAmelCase__					)





@require_torch
class     __a     (       unittest.TestCase ):
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  ErnieMModel.from_pretrained('susnato/ernie-m-base_pytorch'					)
        model.eval()
        lowercase__:  Union[str, Any]         =  torch.tensor([[0, 1, 2, 3, 4, 5]]					)
        lowercase__:  str         =  model(lowerCAmelCase__					)[0]

        # TODO Replace vocab size
        lowercase__:  Union[str, Any]         =  768

        lowercase__:  Any         =  torch.Size((1, 6, hidden_size)					)
        self.assertEqual(output.shape							,					lowerCAmelCase__					)

        lowercase__:  str         =  torch.tensor(
            [[[-0.0_0_1_2, 0.1_2_4_5, -0.0_2_1_4], [-0.0_7_4_2, 0.0_2_4_4, -0.0_7_7_1], [-0.0_3_3_3, 0.1_1_6_4, -0.1_5_5_4]]]					)

        self.assertTrue(torch.allclose(output[:, :3, :3]							,					lowerCAmelCase__							,					atol=1E-3					)					)


