code: 
  
  
  import unittest
  
  import numpy as np
  import timeout_decorator  # noqa
  
  from transformers import OPTConfig, is_flax_available
  from transformers.testing_utils import require_flax, require_sentencepiece, slow
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
  
  
  if is_flax_available():
      import os
  
      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      lowerCAmelCase__   	=				'platform'
  
      import jax
      import jax.numpy as jnp
  
      from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						List[str]=None       ,							UpperCamelCase__  :						str=None		):
         if attention_mask is None:
                _A   : Tuple       					=				np.where(input_ids != config.pad_token_id       ,							1       ,							0		)
         return {
             "input_ids": input_ids,
             "attention_mask": attention_mask,
         }
  
  
  
  
  
  
  
  @require_flax
  class   lowerCAmelCase__ :
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase=1_3				,		__lowerCamelCase=7				,		__lowerCamelCase=True				,		__lowerCamelCase=False				,		__lowerCamelCase=9_9				,		__lowerCamelCase=1_6				,		__lowerCamelCase=2				,		__lowerCamelCase=4				,		__lowerCamelCase=4				,		__lowerCamelCase="gelu"				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.1				,		__lowerCamelCase=2_0				,		__lowerCamelCase=2				,		__lowerCamelCase=1				,		__lowerCamelCase=0				,		__lowerCamelCase=1_6				,		__lowerCamelCase=1_6				,		__lowerCamelCase=0.0_2				,		)    ->       Dict:
              _A   : Optional[Any]       					=				parent
              _A   : Dict       					=				batch_size
              _A   : Union[str, Any]       					=				seq_length
              _A   : Dict       					=				is_training
              _A   : Optional[Any]       					=				use_labels
              _A   : Optional[Any]       					=				vocab_size
              _A   : Union[str, Any]       					=				hidden_size
              _A   : Dict       					=				num_hidden_layers
              _A   : List[Any]       					=				num_attention_heads
              _A   : List[str]       					=				intermediate_size
              _A   : Optional[int]       					=				hidden_act
              _A   : Any       					=				hidden_dropout_prob
              _A   : Tuple       					=				attention_probs_dropout_prob
              _A   : Tuple       					=				max_position_embeddings
              _A   : Optional[Any]       					=				eos_token_id
              _A   : Dict       					=				pad_token_id
              _A   : Dict       					=				bos_token_id
              _A   : Optional[int]       					=				embed_dim
              _A   : List[str]       					=				word_embed_proj_dim
              _A   : Union[str, Any]       					=				initializer_range
              _A   : Union[str, Any]       					=				False
  
  
       def 				_lowerCamelCase				(		self)    ->       Dict:
              _A   : Any       					=				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,		self.vocab_size)				,		3				,		self.vocab_size)
              _A   : Dict       					=				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,		dtype=np.intaa))				,		-1)
  
              _A   : str       					=				OPTConfig(
                  vocab_size=self.vocab_size				,		hidden_size=self.hidden_size				,		num_hidden_layers=self.num_hidden_layers				,		num_attention_heads=self.num_attention_heads				,		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_id=self.eos_token_id				,		bos_token_id=self.bos_token_id				,		pad_token_id=self.pad_token_id				,		embed_dim=self.embed_dim				,		is_encoder_decoder=__lowerCamelCase				,		word_embed_proj_dim=self.word_embed_proj_dim				,		initializer_range=self.initializer_range				,		use_cache=__lowerCamelCase				,		)
              _A   : str       					=				prepare_opt_inputs_dict(__lowerCamelCase				,		__lowerCamelCase)
              return config, inputs_dict
  
  
       def 				_lowerCamelCase				(		self)    ->       Dict:
              _A					,    _A   : Any       					=				self.prepare_config_and_inputs()
              return config, inputs_dict
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Dict:
              _A   : int       					=				2_0
              _A   : Optional[int]       					=				model_class_name(__lowerCamelCase)
  
              _A   : List[Any]       					=				inputs_dict["input_ids"]
              _A   : List[str]       					=				inputs_dict["attention_mask"]
  
              _A   : Any       					=				model.init_cache(input_ids.shape[0]				,		__lowerCamelCase)
              _A   : Any       					=				jnp.ones((input_ids.shape[0], max_length)				,		dtype="i4")
  
              _A   : List[Any]       					=				jnp.broadcast_to(
                  jnp.arange(input_ids.shape[-1] - 1)[None, :]				,		(input_ids.shape[0], input_ids.shape[-1] - 1)				,		)
              _A   : Tuple       					=				model(
                  input_ids[:, :-1]				,		attention_mask=__lowerCamelCase				,		past_key_values=__lowerCamelCase				,		position_ids=__lowerCamelCase				,		)
  
              _A   : Union[str, Any]       					=				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,		dtype="i4")
              _A   : Dict       					=				model(
                  input_ids[:, -1:]				,		attention_mask=__lowerCamelCase				,		past_key_values=outputs_cache.past_key_values				,		position_ids=__lowerCamelCase				,		)
  
              _A   : Dict       					=				model(__lowerCamelCase)
  
              _A   : int       					=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
              self.parent.assertTrue(diff < 1e-3				,		msg=F"Max diff is {diff}")
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Union[str, Any]:
              _A   : int       					=				2_0
              _A   : Optional[int]       					=				model_class_name(__lowerCamelCase)
  
              _A					,    _A   : Optional[int]       					=				(
                  inputs_dict["input_ids"],
                  inputs_dict["attention_mask"],
              )
  
              _A   : Dict       					=				jnp.concatenate(
                  [
                      attention_mask,
                      jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])),
                  ]				,		axis=-1				,		)
  
              _A   : List[Any]       					=				model.init_cache(input_ids.shape[0]				,		__lowerCamelCase)
              _A   : Union[str, Any]       					=				jnp.broadcast_to(
                  jnp.arange(input_ids.shape[-1] - 1)[None, :]				,		(input_ids.shape[0], input_ids.shape[-1] - 1)				,		)
  
              _A   : int       					=				model(
                  input_ids[:, :-1]				,		attention_mask=__lowerCamelCase				,		past_key_values=__lowerCamelCase				,		position_ids=__lowerCamelCase				,		)
              _A   : Tuple       					=				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,		dtype="i4")
              _A   : Optional[Any]       					=				model(
                  input_ids[:, -1:]				,		past_key_values=outputs_cache.past_key_values				,		attention_mask=__lowerCamelCase				,		position_ids=__lowerCamelCase				,		)
  
              _A   : Any       					=				model(__lowerCamelCase				,		attention_mask=__lowerCamelCase)
  
              _A   : Optional[int]       					=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
              self.parent.assertTrue(diff < 1e-3				,		msg=F"Max diff is {diff}")
  
  
  
  
  
  
  
  @require_flax
  class   lowerCAmelCase__ (       a  ,					unittest.TestCase  ,					a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
       __SCREAMING_SNAKE_CASE						    =				() if is_flax_available() else ()
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              _A   : str       					=				FlaxOPTModelTester(self)
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[Any]:
              _A					,    _A   : List[Any]       					=				self.model_tester.prepare_config_and_inputs()
              for model_class in self.all_model_classes:
                     self.model_tester.check_use_cache_forward(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              _A					,    _A   : List[Any]       					=				self.model_tester.prepare_config_and_inputs()
              for model_class in self.all_model_classes:
                     self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
  
  
       @slow
       def 				_lowerCamelCase				(		self)    ->       Optional[Any]:
              for model_class_name in self.all_model_classes:
                     _A   : List[Any]       					=				model_class_name.from_pretrained("facebook/opt-125m")
                     _A   : Optional[int]       					=				np.ones((1, 1)) * model.config.eos_token_id
                     _A   : List[str]       					=				model(__lowerCamelCase)
                     self.assertIsNotNone(__lowerCamelCase)
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_flax
  class   lowerCAmelCase__ (       unittest.TestCase):
  
  
  
  
       '''simple docstring'''
  
  
       @slow
       def 				_lowerCamelCase				(		self)    ->       List[str]:
              _A   : List[str]       					=				FlaxOPTModel.from_pretrained("facebook/opt-350m")
              _A   : Union[str, Any]       					=				jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]])
              _A   : Any       					=				model(input_ids=__lowerCamelCase).last_hidden_state
              _A   : str       					=				(1, 1_1, 5_1_2)
              self.assertEqual(output.shape				,		__lowerCamelCase)
              _A   : Any       					=				jnp.array(
                  [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]])
              self.assertTrue(jnp.allclose(output[:, :3, :3]				,		__lowerCamelCase				,		atol=4e-2))
  
  
  
  
  
  
  
  @require_flax
  @slow
  class   lowerCAmelCase__ (       unittest.TestCase):
  
  
  
  
       '''simple docstring'''
  
  
       def 				_lowerCamelCase				(		self)    ->       Any:
              super().setUp()
              _A   : Optional[int]       					=				"facebook/opt-350m"
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              _A   : Tuple       					=				FlaxOPTForCausalLM.from_pretrained(self.path_model)
              _A   : Optional[int]       					=				GPTaTokenizer.from_pretrained(self.path_model)
  
              _A   : str       					=				[
                  "Today is a beautiful day and I want to",
                  "In the city of",
                  "Paris is the capital of France and",
                  "Computers and mobile phones have taken",
              ]
              # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
              _A   : Optional[int]       					=				tokenizer(__lowerCamelCase				,		return_tensors="jax"				,		padding=__lowerCamelCase				,		add_special_tokens=__lowerCamelCase)
              _A   : Tuple       					=				model(inputs.input_ids				,		attention_mask=inputs.attention_mask)[0].mean(axis=-1)
              _A   : str       					=				jnp.array(
                  [
                      [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                      [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
                      [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
                      [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
                  ])
              self.assertTrue(jnp.allclose(__lowerCamelCase				,		__lowerCamelCase				,		atol=4e-2))
  
              _A   : Any       					=				jax.jit(__lowerCamelCase)
              _A   : Any       					=				model(inputs.input_ids				,		attention_mask=inputs.attention_mask)[0].mean(axis=-1)
              self.assertTrue(jnp.allclose(__lowerCamelCase				,		__lowerCamelCase				,		atol=4e-2))
  
  
  
  
  
  
  
  @require_flax
  @slow
  class   lowerCAmelCase__ (       unittest.TestCase):
  
  
  
  
       '''simple docstring'''
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       str:
              return [
                  "Today is a beautiful day and I want",
                  "In the city of",
                  "Paris is the capital of France and",
                  "Computers and mobile phones have taken",
              ]
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              _A   : List[str]       					=				"facebook/opt-125m"
  
              _A   : List[str]       					=				[
                  "Today is a beautiful day and I want to",
                  "In the city of New York, the city",
                  "Paris is the capital of France and the capital",
                  "Computers and mobile phones have taken over the",
              ]
  
              _A   : str       					=				[]
  
              _A   : Optional[int]       					=				FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase)
              _A   : Tuple       					=				GPTaTokenizer.from_pretrained(__lowerCamelCase)
  
              for prompt in self.prompts:
                     _A   : Union[str, Any]       					=				tokenizer(__lowerCamelCase				,		return_tensors="jax").input_ids
  
                     _A   : List[Any]       					=				model.generate(__lowerCamelCase				,		max_length=1_0)
                     _A   : List[str]       					=				generated_ids[0]
  
                     _A   : List[Any]       					=				tokenizer.batch_decode(__lowerCamelCase				,		skip_special_tokens=__lowerCamelCase)
                     predicted_outputs += generated_string
  
              self.assertListEqual(__lowerCamelCase				,		__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       List[str]:
              _A   : List[Any]       					=				"facebook/opt-350m"
  
              _A   : Dict       					=				[
                  "Today is a beautiful day and I want to",
                  "In the city of San Francisco, the city",
                  "Paris is the capital of France and the capital",
                  "Computers and mobile phones have taken over the",
              ]
  
              _A   : str       					=				[]
              _A   : Tuple       					=				FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase)
              _A   : int       					=				GPTaTokenizer.from_pretrained(__lowerCamelCase)
  
              for prompt in self.prompts:
                     _A   : Union[str, Any]       					=				tokenizer(__lowerCamelCase				,		return_tensors="jax").input_ids
  
                     _A   : List[str]       					=				model.generate(__lowerCamelCase				,		max_length=1_0)
                     _A   : Optional[int]       					=				generated_ids[0]
  
                     _A   : Union[str, Any]       					=				tokenizer.batch_decode(__lowerCamelCase				,		skip_special_tokens=__lowerCamelCase)
                     predicted_outputs += generated_string
  
              self.assertListEqual(__lowerCamelCase				,		__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              _A   : Union[str, Any]       					=				"facebook/opt-125m"
              _A   : int       					=				[
                  "Today is a beautiful day and I want to thank",
                  "In the city of Rome Canaver Canaver Canaver Canaver",
              ]
              _A   : List[str]       					=				FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase)
              _A   : List[str]       					=				GPTaTokenizer.from_pretrained(__lowerCamelCase)
              _A   : Optional[Any]       					=				tokenizer(
                  [
                      "Today is a beautiful day and I want to",
                      "In the city of",
                  ]				,		return_tensors="jax"				,		padding=__lowerCamelCase				,		)
  
              _A   : List[str]       					=				jax.jit(model.generate)
  
              _A   : Tuple       					=				jit_generate(inputs["input_ids"]				,		attention_mask=inputs["attention_mask"]).sequences
  
              _A   : Any       					=				tokenizer.batch_decode(__lowerCamelCase				,		skip_special_tokens=__lowerCamelCase)
  
              self.assertIsNotNone(__lowerCamelCase				,		__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       Dict:
              _A   : Optional[int]       					=				"facebook/opt-350m"
  
              _A   : Union[str, Any]       					=				GPTaTokenizer.from_pretrained(__lowerCamelCase)
              _A   : Dict       					=				FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase)
  
              _A   : List[str]       					=				"left"
  
              # use different length sentences to test batching
              _A   : Tuple       					=				[
                  "Hello, my dog is a little",
                  "Today, I",
              ]
  
              _A   : int       					=				tokenizer(__lowerCamelCase				,		return_tensors="jax"				,		padding=__lowerCamelCase)
              _A   : Optional[Any]       					=				inputs["input_ids"]
  
              _A   : Union[str, Any]       					=				model.generate(input_ids=__lowerCamelCase				,		attention_mask=inputs["attention_mask"]				,		trace=__lowerCamelCase)
  
              _A   : List[str]       					=				tokenizer(sentences[0]				,		return_tensors="jax").input_ids
              _A   : List[Any]       					=				model.generate(input_ids=__lowerCamelCase)
  
              _A   : int       					=				inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
              _A   : Any       					=				tokenizer(sentences[1]				,		return_tensors="jax").input_ids
              _A   : str       					=				model.generate(input_ids=__lowerCamelCase				,		max_length=model.config.max_length - num_paddings)
  
              _A   : Optional[Any]       					=				tokenizer.batch_decode(outputs[0]				,		skip_special_tokens=__lowerCamelCase)
              _A   : Tuple       					=				tokenizer.decode(output_non_padded[0][0]				,		skip_special_tokens=__lowerCamelCase)
              _A   : List[Any]       					=				tokenizer.decode(output_padded[0][0]				,		skip_special_tokens=__lowerCamelCase)
  
              _A   : int       					=				[
                  "Hello, my dog is a little bit of a dork.\nI'm a little bit",
                  "Today, I was in the middle of a conversation with a friend about the",
              ]
              self.assertListEqual(__lowerCamelCase				,		__lowerCamelCase)
              self.assertListEqual(__lowerCamelCase				,		[non_padded_sentence, padded_sentence])
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCAmelCase__   	=				{
      'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['SegformerFeatureExtractor']
      lowerCAmelCase__   	=				['SegformerImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SegformerDecodeHead',
          'SegformerForImageClassification',
          'SegformerForSemanticSegmentation',
          'SegformerLayer',
          'SegformerModel',
          'SegformerPreTrainedModel',
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSegformerDecodeHead',
          'TFSegformerForImageClassification',
          'TFSegformerForSemanticSegmentation',
          'TFSegformerModel',
          'TFSegformerPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .feature_extraction_segformer import SegformerFeatureExtractor
          from .image_processing_segformer import SegformerImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_segformer import (
              SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              SegformerDecodeHead,
              SegformerForImageClassification,
              SegformerForSemanticSegmentation,
              SegformerLayer,
              SegformerModel,
              SegformerPreTrainedModel,
          )
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_segformer import (
              TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFSegformerDecodeHead,
              TFSegformerForImageClassification,
              TFSegformerForSemanticSegmentation,
              TFSegformerModel,
              TFSegformerPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  