code: 
  
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  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
        SCREAMING_SNAKE_CASE__		:       List[Any]   							=							"""platform"""
  
        import jax
        import jax.numpy as jnp
  
        from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=None   ,       __lowerCamelCase=None					)   -> Tuple:
  
       '''simple docstring'''
       if attention_mask is None:
            UpperCAmelCase__			:     Optional[int]										= np.where(input_ids != config.pad_token_id   ,       1   ,       0					)
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
       }
  
  
  
  
  
  
  
  @require_flax
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=13  ,	_lowerCAmelCase=7  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase=False  ,	_lowerCAmelCase=99  ,	_lowerCAmelCase=16  ,	_lowerCAmelCase=2  ,	_lowerCAmelCase=4  ,	_lowerCAmelCase=4  ,	_lowerCAmelCase="gelu"  ,	_lowerCAmelCase=0.1  ,	_lowerCAmelCase=0.1  ,	_lowerCAmelCase=20  ,	_lowerCAmelCase=2  ,	_lowerCAmelCase=1  ,	_lowerCAmelCase=0  ,	_lowerCAmelCase=16  ,	_lowerCAmelCase=16  ,	_lowerCAmelCase=0.0_2  ,	):
            UpperCAmelCase__			:     Optional[int]										= parent
            UpperCAmelCase__			:     Any										= batch_size
            UpperCAmelCase__			:     int										= seq_length
            UpperCAmelCase__			:     str										= is_training
            UpperCAmelCase__			:     Any										= use_labels
            UpperCAmelCase__			:     int										= vocab_size
            UpperCAmelCase__			:     Dict										= hidden_size
            UpperCAmelCase__			:     Union[str, Any]										= num_hidden_layers
            UpperCAmelCase__			:     List[str]										= num_attention_heads
            UpperCAmelCase__			:     List[str]										= intermediate_size
            UpperCAmelCase__			:     List[Any]										= hidden_act
            UpperCAmelCase__			:     List[Any]										= hidden_dropout_prob
            UpperCAmelCase__			:     Optional[Any]										= attention_probs_dropout_prob
            UpperCAmelCase__			:     Tuple										= max_position_embeddings
            UpperCAmelCase__			:     str										= eos_token_id
            UpperCAmelCase__			:     Any										= pad_token_id
            UpperCAmelCase__			:     int										= bos_token_id
            UpperCAmelCase__			:     str										= embed_dim
            UpperCAmelCase__			:     int										= word_embed_proj_dim
            UpperCAmelCase__			:     str										= initializer_range
            UpperCAmelCase__			:     Union[str, Any]										= False
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Tuple										= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,	self.vocab_size		)  ,	3  ,	self.vocab_size		)
            UpperCAmelCase__			:     Dict										= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,	dtype=np.intaa		))  ,	-1		)
  
            UpperCAmelCase__			:     List[Any]										= 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  ,	)
            UpperCAmelCase__			:     Optional[int]										= prepare_opt_inputs_dict(_lowerCAmelCase  ,	_lowerCAmelCase		)
            return config, inputs_dict
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     str										= self.prepare_config_and_inputs()
            return config, inputs_dict
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     Union[str, Any]										= 20
            UpperCAmelCase__			:     Optional[Any]										= model_class_name(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     Optional[Any]										= inputs_dict["""input_ids"""]
            UpperCAmelCase__			:     Dict										= inputs_dict["""attention_mask"""]
  
            UpperCAmelCase__			:     Tuple										= model.init_cache(input_ids.shape[0]  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     Optional[Any]										= jnp.ones((input_ids.shape[0], max_length)  ,	dtype="""i4"""		)
  
            UpperCAmelCase__			:     List[Any]										= jnp.broadcast_to(
                jnp.arange(input_ids.shape[-1] - 1		)[None, :]  ,	(input_ids.shape[0], input_ids.shape[-1] - 1)  ,	)
            UpperCAmelCase__			:     Optional[int]										= model(
                input_ids[:, :-1]  ,	attention_mask=_lowerCAmelCase  ,	past_key_values=_lowerCAmelCase  ,	position_ids=_lowerCAmelCase  ,	)
  
            UpperCAmelCase__			:     Any										= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,	dtype="""i4"""		)
            UpperCAmelCase__			:     int										= model(
                input_ids[:, -1:]  ,	attention_mask=_lowerCAmelCase  ,	past_key_values=outputs_cache.past_key_values  ,	position_ids=_lowerCAmelCase  ,	)
  
            UpperCAmelCase__			:     Tuple										= model(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     str										= 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      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     Dict										= 20
            UpperCAmelCase__			:     List[Any]										= model_class_name(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     str										= (
                inputs_dict["""input_ids"""],
                inputs_dict["""attention_mask"""],
            )
  
            UpperCAmelCase__			:     int										= jnp.concatenate(
                [
                    attention_mask,
                    jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])		),
                ]  ,	axis=-1  ,	)
  
            UpperCAmelCase__			:     Dict										= model.init_cache(input_ids.shape[0]  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     int										= jnp.broadcast_to(
                jnp.arange(input_ids.shape[-1] - 1		)[None, :]  ,	(input_ids.shape[0], input_ids.shape[-1] - 1)  ,	)
  
            UpperCAmelCase__			:     Tuple										= model(
                input_ids[:, :-1]  ,	attention_mask=_lowerCAmelCase  ,	past_key_values=_lowerCAmelCase  ,	position_ids=_lowerCAmelCase  ,	)
            UpperCAmelCase__			:     Tuple										= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,	dtype="""i4"""		)
            UpperCAmelCase__			:     Optional[int]										= model(
                input_ids[:, -1:]  ,	past_key_values=outputs_cache.past_key_values  ,	attention_mask=_lowerCAmelCase  ,	position_ids=_lowerCAmelCase  ,	)
  
            UpperCAmelCase__			:     Tuple										= model(_lowerCAmelCase  ,	attention_mask=_lowerCAmelCase		)
  
            UpperCAmelCase__			:     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 							UpperCAmelCase_     (		__lowerCamelCase       ,			unittest.TestCase       ,			__lowerCamelCase		):
       __lowerCamelCase										=     (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
       __lowerCamelCase										=     () if is_flax_available() else ()
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Any										= FlaxOPTModelTester(self		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     int										= 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      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Tuple										= 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      __UpperCAmelCase     (							self		):
            for model_class_name in self.all_model_classes:
                 UpperCAmelCase__			:     int										= model_class_name.from_pretrained("""facebook/opt-125m"""		)
                 UpperCAmelCase__			:     Any										= np.ones((1, 1)		) * model.config.eos_token_id
                 UpperCAmelCase__			:     List[Any]										= model(_lowerCAmelCase		)
                 self.assertIsNotNone(_lowerCAmelCase		)
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_flax
  class 							UpperCAmelCase_     (		unittest.TestCase		):
  
  
  
  
       @slow
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Optional[int]										= FlaxOPTModel.from_pretrained("""facebook/opt-350m"""		)
            UpperCAmelCase__			:     List[Any]										= jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
            UpperCAmelCase__			:     Any										= model(input_ids=_lowerCAmelCase		).last_hidden_state
            UpperCAmelCase__			:     Union[str, Any]										= (1, 11, 512)
            self.assertEqual(output.shape  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     List[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 							UpperCAmelCase_     (		unittest.TestCase		):
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            super().setUp()
            UpperCAmelCase__			:     Optional[int]										= """facebook/opt-350m"""
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Dict										= FlaxOPTForCausalLM.from_pretrained(self.path_model		)
            UpperCAmelCase__			:     str										= GPTaTokenizer.from_pretrained(self.path_model		)
  
            UpperCAmelCase__			:     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
            UpperCAmelCase__			:     List[Any]										= tokenizer(_lowerCAmelCase  ,	return_tensors="""jax"""  ,	padding=_lowerCAmelCase  ,	add_special_tokens=_lowerCAmelCase		)
            UpperCAmelCase__			:     List[Any]										= model(inputs.input_ids  ,	attention_mask=inputs.attention_mask		)[0].mean(axis=-1		)
            UpperCAmelCase__			:     Tuple										= jnp.array(
                [
                    [1.3_8_5_1, -13.8923, -10.5229, -10.7533, -0.2_3_0_9, -10.2384, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                    [-4.7_0_7_3, -10.6276, -3.9_4_1_5, -21.5242, -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, -14.1650, 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, -10.7926, -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		)		)
  
            UpperCAmelCase__			:     str										= jax.jit(_lowerCAmelCase		)
            UpperCAmelCase__			:     Optional[int]										= 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 							UpperCAmelCase_     (		unittest.TestCase		):
  
  
  
  
       @property
       def      __UpperCAmelCase     (							self		):
            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      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     List[Any]										= """facebook/opt-125m"""
  
            UpperCAmelCase__			:     List[Any]										= [
                """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""",
            ]
  
            UpperCAmelCase__			:     Optional[Any]										= []
  
            UpperCAmelCase__			:     List[Any]										= FlaxOPTForCausalLM.from_pretrained(_lowerCAmelCase		)
            UpperCAmelCase__			:     Any										= GPTaTokenizer.from_pretrained(_lowerCAmelCase		)
  
            for prompt in self.prompts:
                 UpperCAmelCase__			:     List[Any]										= tokenizer(_lowerCAmelCase  ,	return_tensors="""jax"""		).input_ids
  
                 UpperCAmelCase__			:     List[str]										= model.generate(_lowerCAmelCase  ,	max_length=10		)
                 UpperCAmelCase__			:     List[str]										= generated_ids[0]
  
                 UpperCAmelCase__			:     str										= tokenizer.batch_decode(_lowerCAmelCase  ,	skip_special_tokens=_lowerCAmelCase		)
                 predicted_outputs += generated_string
  
            self.assertListEqual(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     List[str]										= """facebook/opt-350m"""
  
            UpperCAmelCase__			:     Tuple										= [
                """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""",
            ]
  
            UpperCAmelCase__			:     str										= []
            UpperCAmelCase__			:     List[str]										= FlaxOPTForCausalLM.from_pretrained(_lowerCAmelCase		)
            UpperCAmelCase__			:     Any										= GPTaTokenizer.from_pretrained(_lowerCAmelCase		)
  
            for prompt in self.prompts:
                 UpperCAmelCase__			:     str										= tokenizer(_lowerCAmelCase  ,	return_tensors="""jax"""		).input_ids
  
                 UpperCAmelCase__			:     Optional[Any]										= model.generate(_lowerCAmelCase  ,	max_length=10		)
                 UpperCAmelCase__			:     Any										= generated_ids[0]
  
                 UpperCAmelCase__			:     Tuple										= tokenizer.batch_decode(_lowerCAmelCase  ,	skip_special_tokens=_lowerCAmelCase		)
                 predicted_outputs += generated_string
  
            self.assertListEqual(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Union[str, Any]										= """facebook/opt-125m"""
            UpperCAmelCase__			:     Optional[int]										= [
                """Today is a beautiful day and I want to thank""",
                """In the city of Rome Canaver Canaver Canaver Canaver""",
            ]
            UpperCAmelCase__			:     Any										= FlaxOPTForCausalLM.from_pretrained(_lowerCAmelCase		)
            UpperCAmelCase__			:     Union[str, Any]										= GPTaTokenizer.from_pretrained(_lowerCAmelCase		)
            UpperCAmelCase__			:     Union[str, Any]										= tokenizer(
                [
                    """Today is a beautiful day and I want to""",
                    """In the city of""",
                ]  ,	return_tensors="""jax"""  ,	padding=_lowerCAmelCase  ,	)
  
            UpperCAmelCase__			:     int										= jax.jit(model.generate		)
  
            UpperCAmelCase__			:     int										= jit_generate(inputs["""input_ids"""]  ,	attention_mask=inputs["""attention_mask"""]		).sequences
  
            UpperCAmelCase__			:     str										= tokenizer.batch_decode(_lowerCAmelCase  ,	skip_special_tokens=_lowerCAmelCase		)
  
            self.assertIsNotNone(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     List[Any]										= """facebook/opt-350m"""
  
            UpperCAmelCase__			:     Any										= GPTaTokenizer.from_pretrained(_lowerCAmelCase		)
            UpperCAmelCase__			:     Optional[Any]										= FlaxOPTForCausalLM.from_pretrained(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     List[Any]										= """left"""
  
            # use different length sentences to test batching
            UpperCAmelCase__			:     Union[str, Any]										= [
                """Hello, my dog is a little""",
                """Today, I""",
            ]
  
            UpperCAmelCase__			:     str										= tokenizer(_lowerCAmelCase  ,	return_tensors="""jax"""  ,	padding=_lowerCAmelCase		)
            UpperCAmelCase__			:     List[Any]										= inputs["""input_ids"""]
  
            UpperCAmelCase__			:     List[Any]										= model.generate(input_ids=_lowerCAmelCase  ,	attention_mask=inputs["""attention_mask"""]  ,	trace=_lowerCAmelCase		)
  
            UpperCAmelCase__			:     Optional[int]										= tokenizer(sentences[0]  ,	return_tensors="""jax"""		).input_ids
            UpperCAmelCase__			:     List[Any]										= model.generate(input_ids=_lowerCAmelCase		)
  
            UpperCAmelCase__			:     Optional[int]										= inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].sum()
            UpperCAmelCase__			:     List[str]										= tokenizer(sentences[1]  ,	return_tensors="""jax"""		).input_ids
            UpperCAmelCase__			:     Optional[int]										= model.generate(input_ids=_lowerCAmelCase  ,	max_length=model.config.max_length - num_paddings		)
  
            UpperCAmelCase__			:     Any										= tokenizer.batch_decode(outputs[0]  ,	skip_special_tokens=_lowerCAmelCase		)
            UpperCAmelCase__			:     int										= tokenizer.decode(output_non_padded[0][0]  ,	skip_special_tokens=_lowerCAmelCase		)
            UpperCAmelCase__			:     List[str]										= tokenizer.decode(output_padded[0][0]  ,	skip_special_tokens=_lowerCAmelCase		)
  
            UpperCAmelCase__			:     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: 
  
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							logging.get_logger(__name__)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
  
  
  
  
       def __init__(							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            warnings.warn(
                """The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
                """ use ViltImageProcessor instead."""  ,	_lowerCAmelCase  ,	)
            super().__init__(*_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
  
  
  
  
  
  