


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




def   snake_case_	(    self     ,				snake_case     ,				snake_case     ,				snake_case     ,				snake_case	)    ->							List[Any]:
       lowercase__:  Tuple         =  image.to(device=snake_case	)
       lowercase__:  List[str]         =  image  # take image as image_embeddings
       lowercase__:  Tuple         =  image_embeddings.unsqueeze(1	)

       # duplicate image embeddings for each generation per prompt, using mps friendly method
       lowercase__     ,		lowercase__     ,		lowercase__:  List[Any]         =  image_embeddings.shape
       lowercase__:  Union[str, Any]         =  image_embeddings.repeat(1     ,				snake_case     ,				1	)
       lowercase__:  Optional[Any]         =  image_embeddings.view(bs_embed * num_images_per_prompt     ,				snake_case     ,				-1	)

       if do_classifier_free_guidance:
              lowercase__:  Union[str, Any]         =  torch.zeros_like(snake_case	)

              # 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
              lowercase__:  Optional[int]         =  torch.cat([uncond_embeddings, image_embeddings]	)

       return image_embeddings





class     __a     (       __UpperCamelCase ):
 def __init__(  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__ = None							,					)   ->							List[str]:




        '''simple docstring'''
        super().__init__()

        lowercase__:  Tuple         =  {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

        lowercase__:  int         =  decoder_pipe_kwargs.get('torch_dtype'							,					lowerCAmelCase__					) or prior.dtype

        lowercase__:  Optional[Any]         =  StableDiffusionImageVariationPipeline.from_pretrained(
            'lambdalabs/sd-image-variations-diffusers'							,					**lowerCAmelCase__					)

        # replace `_encode_image` method
        lowercase__:  str         =  types.MethodType(_encode_image							,					self.decoder_pipe					)

        self.register_modules(
            prior=lowerCAmelCase__							,					tokenizer=lowerCAmelCase__							,					text_encoder=lowerCAmelCase__							,					prior_scheduler=lowerCAmelCase__							,					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__ = None							,					lowerCAmelCase__ = None							,					)   ->							Any:




        '''simple docstring'''
        if text_model_output is None:
               lowercase__:  Union[str, Any]         =  len(lowerCAmelCase__					) if isinstance(lowerCAmelCase__							,					lowerCAmelCase__					) else 1
               # get prompt text embeddings
               lowercase__:  Optional[Any]         =  self.tokenizer(
                   lowerCAmelCase__							,					padding='max_length'							,					max_length=self.tokenizer.model_max_length							,					return_tensors='pt'							,					)
               lowercase__:  str         =  text_inputs.input_ids
               lowercase__:  str         =  text_inputs.attention_mask.bool().to(lowerCAmelCase__					)

               if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                      lowercase__:  List[str]         =  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}'					)
                      lowercase__:  List[str]         =  text_input_ids[:, : self.tokenizer.model_max_length]

               lowercase__:  Optional[int]         =  self.text_encoder(text_input_ids.to(lowerCAmelCase__					)					)

               lowercase__:  Tuple         =  text_encoder_output.text_embeds
               lowercase__:  Dict         =  text_encoder_output.last_hidden_state

        else:
               lowercase__:  Any         =  text_model_output[0].shape[0]
               lowercase__     ,		lowercase__:  Union[str, Any]         =  text_model_output[0], text_model_output[1]
               lowercase__:  List[str]         =  text_attention_mask

        lowercase__:  Tuple         =  text_embeddings.repeat_interleave(lowerCAmelCase__							,					dim=0					)
        lowercase__:  Tuple         =  text_encoder_hidden_states.repeat_interleave(lowerCAmelCase__							,					dim=0					)
        lowercase__:  Any         =  text_mask.repeat_interleave(lowerCAmelCase__							,					dim=0					)

        if do_classifier_free_guidance:
               lowercase__:  Any         =  [''] * batch_size

               lowercase__:  Optional[Any]         =  self.tokenizer(
                   lowerCAmelCase__							,					padding='max_length'							,					max_length=self.tokenizer.model_max_length							,					truncation=lowerCAmelCase__							,					return_tensors='pt'							,					)
               lowercase__:  Optional[int]         =  uncond_input.attention_mask.bool().to(lowerCAmelCase__					)
               lowercase__:  int         =  self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase__					)					)

               lowercase__:  Tuple         =  uncond_embeddings_text_encoder_output.text_embeds
               lowercase__:  Tuple         =  uncond_embeddings_text_encoder_output.last_hidden_state

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

               lowercase__:  Tuple         =  uncond_embeddings.shape[1]
               lowercase__:  List[Any]         =  uncond_embeddings.repeat(1							,					lowerCAmelCase__					)
               lowercase__:  List[Any]         =  uncond_embeddings.view(batch_size * num_images_per_prompt							,					lowerCAmelCase__					)

               lowercase__:  Union[str, Any]         =  uncond_text_encoder_hidden_states.shape[1]
               lowercase__:  int         =  uncond_text_encoder_hidden_states.repeat(1							,					lowerCAmelCase__							,					1					)
               lowercase__:  Any         =  uncond_text_encoder_hidden_states.view(
                   batch_size * num_images_per_prompt							,					lowerCAmelCase__							,					-1					)
               lowercase__:  Any         =  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
               lowercase__:  Optional[int]         =  torch.cat([uncond_embeddings, text_embeddings]					)
               lowercase__:  int         =  torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

               lowercase__:  int         =  torch.cat([uncond_text_mask, text_mask]					)

        return text_embeddings, text_encoder_hidden_states, text_mask
 @property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[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 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Union[str, Any]:




        '''simple docstring'''
        if latents is None:
               lowercase__:  str         =  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}'					)
               lowercase__:  int         =  latents.to(lowerCAmelCase__					)

        lowercase__:  Dict         =  latents * scheduler.init_noise_sigma
        return latents
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__ = None					)   ->							List[Any]:




        '''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							,					)   ->							List[str]:




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

        lowercase__:  str         =  self._execution_device

        lowercase__:  List[str]         =  batch_size * num_images_per_prompt

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

        lowercase__     ,		lowercase__     ,		lowercase__:  List[Any]         =  self._encode_prompt(
            lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)

        # prior

        self.prior_scheduler.set_timesteps(lowerCAmelCase__							,					device=lowerCAmelCase__					)
        lowercase__:  List[str]         =  self.prior_scheduler.timesteps

        lowercase__:  Dict         =  self.prior.config.embedding_dim

        lowercase__:  List[str]         =  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
               lowercase__:  int         =  torch.cat([prior_latents] * 2					) if do_classifier_free_guidance else prior_latents

               lowercase__:  Optional[Any]         =  self.prior(
                   lowerCAmelCase__							,					timestep=lowerCAmelCase__							,					proj_embedding=lowerCAmelCase__							,					encoder_hidden_states=lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					).predicted_image_embedding

               if do_classifier_free_guidance:
                      lowercase__     ,		lowercase__:  List[str]         =  predicted_image_embedding.chunk(2					)
                      lowercase__:  str         =  predicted_image_embedding_uncond + prior_guidance_scale * (
                          predicted_image_embedding_text - predicted_image_embedding_uncond
                      )

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

               lowercase__:  Union[str, Any]         =  self.prior_scheduler.step(
                   lowerCAmelCase__							,					timestep=lowerCAmelCase__							,					sample=lowerCAmelCase__							,					generator=lowerCAmelCase__							,					prev_timestep=lowerCAmelCase__							,					).prev_sample

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

        lowercase__:  Optional[Any]         =  prior_latents

        lowercase__:  int         =  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					)					)


