from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

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


if is_tf_available():
       import tensorflow as tf

       from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : Union[str, Any]						,	A_   : List[Any]=None						,	A_   : Tuple=None					)    ->  Optional[Any]:
  if attention_mask is None:
    __UpperCAmelCase						    =       tf.cast(tf.math.not_equal(A_						,	config.pad_token_id					)						,	tf.inta					)
  return {"input_ids": input_ids, "attention_mask": attention_mask}


@require_tf
class 			UpperCAmelCase__    :



  """simple docstring"""






  lowerCAmelCase__		:	Optional[int]    		= OPTConfig
  lowerCAmelCase__		:	Optional[int]    		= {}
  lowerCAmelCase__		:	List[str]    		= 'gelu'






  def __init__(				self:			Union[str, Any]				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			List[Any]=13				,				__lowerCAmelCase:			Union[str, Any]=7				,				__lowerCAmelCase:			Tuple=True				,				__lowerCAmelCase:			Any=False				,				__lowerCAmelCase:			Optional[int]=99				,				__lowerCAmelCase:			Optional[Any]=16				,				__lowerCAmelCase:			Tuple=2				,				__lowerCAmelCase:			str=4				,				__lowerCAmelCase:			Optional[Any]=4				,				__lowerCAmelCase:			Tuple="gelu"				,				__lowerCAmelCase:			Optional[int]=0.1				,				__lowerCAmelCase:			Any=0.1				,				__lowerCAmelCase:			List[str]=20				,				__lowerCAmelCase:			Dict=2				,				__lowerCAmelCase:			Dict=1				,				__lowerCAmelCase:			Union[str, Any]=0				,				__lowerCAmelCase:			Any=16				,				__lowerCAmelCase:			str=16				,				) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       parent
    __UpperCAmelCase						    =       batch_size
    __UpperCAmelCase						    =       seq_length
    __UpperCAmelCase						    =       is_training
    __UpperCAmelCase						    =       use_labels
    __UpperCAmelCase						    =       vocab_size
    __UpperCAmelCase						    =       hidden_size
    __UpperCAmelCase						    =       num_hidden_layers
    __UpperCAmelCase						    =       num_attention_heads
    __UpperCAmelCase						    =       intermediate_size
    __UpperCAmelCase						    =       hidden_act
    __UpperCAmelCase						    =       hidden_dropout_prob
    __UpperCAmelCase						    =       attention_probs_dropout_prob
    __UpperCAmelCase						    =       max_position_embeddings
    __UpperCAmelCase						    =       eos_token_id
    __UpperCAmelCase						    =       pad_token_id
    __UpperCAmelCase						    =       bos_token_id
    __UpperCAmelCase						    =       embed_dim
    __UpperCAmelCase						    =       word_embed_proj_dim
    __UpperCAmelCase						    =       False






  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       ids_tensor([self.batch_size, self.seq_length - 1]				,				self.vocab_size   )
    __UpperCAmelCase						    =       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )				,				1   )
    __UpperCAmelCase						    =       tf.concat([input_ids, eos_tensor]				,				axis=1   )

    __UpperCAmelCase						    =       self.config_cls(
        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				,				word_embed_proj_dim=self.word_embed_proj_dim				,				is_encoder_decoder=__lowerCAmelCase				,				**self.config_updates				,				)
    __UpperCAmelCase						    =       prepare_opt_inputs_dict(__lowerCAmelCase				,				__lowerCAmelCase   )
    return config, inputs_dict






  def 	_UpperCAmelCase				(				self:			int				,				__lowerCAmelCase:			int				,				__lowerCAmelCase:			Any   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       TFOPTModel(config=__lowerCAmelCase   )
    __UpperCAmelCase						    =       inputs_dict["input_ids"]

    __UpperCAmelCase						    =       input_ids[:1, :]
    __UpperCAmelCase						    =       inputs_dict["attention_mask"][:1, :]
    __UpperCAmelCase						    =       1

    # first forward pass
    __UpperCAmelCase						    =       model(__lowerCAmelCase				,				attention_mask=__lowerCAmelCase				,				use_cache=__lowerCAmelCase   )

    __UpperCAmelCase    ,		__UpperCAmelCase						    =       outputs.to_tuple()

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

    # append to next input_ids and
    __UpperCAmelCase						    =       tf.concat([input_ids, next_tokens]				,				axis=-1   )
    __UpperCAmelCase						    =       tf.concat([attention_mask, next_attn_mask]				,				axis=-1   )

    __UpperCAmelCase						    =       model(__lowerCAmelCase				,				attention_mask=__lowerCAmelCase   )[0]
    __UpperCAmelCase						    =       model(__lowerCAmelCase				,				attention_mask=__lowerCAmelCase				,				past_key_values=__lowerCAmelCase   )[0]

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

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

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






@require_tf
class 			UpperCAmelCase__    (		snake_case		,	snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	List[Any]    		= (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
  lowerCAmelCase__		:	Dict    		= (TFOPTForCausalLM,) if is_tf_available() else ()
  lowerCAmelCase__		:	Optional[int]    		= (
      {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
  )
  lowerCAmelCase__		:	int    		= False
  lowerCAmelCase__		:	str    		= False
  lowerCAmelCase__		:	Dict    		= False
  lowerCAmelCase__		:	Any    		= 10






  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       TFOPTModelTester(self   )
    __UpperCAmelCase						    =       ConfigTester(self				,				config_class=__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Tuple   ) -> List[Any]:




    '''simple docstring'''




    self.config_tester.run_common_tests()






  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> str:




    '''simple docstring'''




    __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs_for_common()

    def _get_word_embedding_weight(__lowerCAmelCase:			Dict				,				__lowerCAmelCase:			List[str]   ):
      if hasattr(__lowerCAmelCase				,				"weight"   ):
        return embedding_layer.weight
      else:
        # Here we build the word embeddings weights if not exists.
        # And then we retry to get the attribute once built.
        model.build()
        if hasattr(__lowerCAmelCase				,				"weight"   ):
          return embedding_layer.weight
        else:
          return None

    for model_class in self.all_model_classes:
      for size in [config.vocab_size - 10, config.vocab_size + 10]:
        # build the embeddings
        __UpperCAmelCase						    =       model_class(config=__lowerCAmelCase   )
        __UpperCAmelCase						    =       _get_word_embedding_weight(__lowerCAmelCase				,				model.get_input_embeddings()   )
        __UpperCAmelCase						    =       _get_word_embedding_weight(__lowerCAmelCase				,				model.get_output_embeddings()   )

        # reshape the embeddings
        model.resize_token_embeddings(__lowerCAmelCase   )
        __UpperCAmelCase						    =       _get_word_embedding_weight(__lowerCAmelCase				,				model.get_input_embeddings()   )
        __UpperCAmelCase						    =       _get_word_embedding_weight(__lowerCAmelCase				,				model.get_output_embeddings()   )

        # check that the resized embeddings size matches the desired size.
        __UpperCAmelCase						    =       size if size is not None else config.vocab_size

        self.assertEqual(new_input_embeddings.shape[0]				,				__lowerCAmelCase   )

        # check that weights remain the same after resizing
        __UpperCAmelCase						    =       True
        for pa, pa in zip(old_input_embeddings.value()				,				new_input_embeddings.value()   ):
          if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
            __UpperCAmelCase						    =       False
        self.assertTrue(__lowerCAmelCase   )

        if old_output_embeddings is not None and new_output_embeddings is not None:
          self.assertEqual(new_output_embeddings.shape[0]				,				__lowerCAmelCase   )

          __UpperCAmelCase						    =       True
          for pa, pa in zip(old_output_embeddings.value()				,				new_output_embeddings.value()   ):
            if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
              __UpperCAmelCase						    =       False
          self.assertTrue(__lowerCAmelCase   )






def       __lowerCAmelCase      (						A_   : Dict					)    ->  Tuple:
  return tf.constant(A_						,	dtype=tf.intaa					)


@require_tf
class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	Optional[int]    		= 99






  def 	_UpperCAmelCase				(				self:			int   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       tf.ones((4, 1)				,				dtype=tf.intaa   ) * 2
    __UpperCAmelCase						    =       tf.concat([ids_tensor((4, 6)				,				self.vocab_size - 3   ) + 3, eos_column_vector]				,				axis=1   )
    __UpperCAmelCase						    =       input_ids.shape[0]
    __UpperCAmelCase						    =       OPTConfig(
        vocab_size=self.vocab_size				,				hidden_size=24				,				num_hidden_layers=2				,				num_attention_heads=2				,				ffn_dim=32				,				max_position_embeddings=48				,				eos_token_id=2				,				pad_token_id=1				,				bos_token_id=0				,				)
    return config, input_ids, batch_size






@require_sentencepiece
@require_tf
class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  @slow
  def 	_UpperCAmelCase				(				self:			str   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       TFOPTModel.from_pretrained("facebook/opt-350m"   )
    __UpperCAmelCase						    =       _long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]   )
    __UpperCAmelCase						    =       tf.not_equal(__lowerCAmelCase				,				model.config.pad_token_id   )
    with tf.GradientTape():
      __UpperCAmelCase						    =       model(input_ids=__lowerCAmelCase				,				attention_mask=__lowerCAmelCase   ).last_hidden_state
    __UpperCAmelCase						    =       (1, 11, 512)
    self.assertEqual(output.shape				,				__lowerCAmelCase   )
    __UpperCAmelCase						    =       tf.constant(
        [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]   )
    self.assertTrue(np.allclose(output[:, :3, :3]				,				__lowerCAmelCase				,				atol=4E-3   )   )

    __UpperCAmelCase						    =       tf.function(__lowerCAmelCase				,				jit_compile=__lowerCAmelCase   )
    __UpperCAmelCase						    =       xla_generate(__lowerCAmelCase				,				__lowerCAmelCase   )[0]
    self.assertTrue(np.allclose(output[:, :3, :3]				,				__lowerCAmelCase				,				atol=4E-2   )   )






@require_tf
@slow
class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			Any   ) -> List[str]:




    '''simple docstring'''




    super().setUp()
    __UpperCAmelCase						    =       "facebook/opt-350m"






  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> Tuple:




    '''simple docstring'''




    __UpperCAmelCase						    =       TFOPTForCausalLM.from_pretrained(self.path_model   )
    __UpperCAmelCase						    =       GPTaTokenizer.from_pretrained(self.path_model   )

    __UpperCAmelCase						    =       [
        "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						    =       tokenizer(__lowerCAmelCase				,				return_tensors="tf"				,				padding=__lowerCAmelCase				,				add_special_tokens=__lowerCAmelCase   )
    __UpperCAmelCase						    =       tf.math.reduce_mean(model(inputs.input_ids				,				attention_mask=inputs.attention_mask   )[0]				,				axis=-1   )
    __UpperCAmelCase						    =       tf.constant(
        [
            [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
            [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
            [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
            [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
        ]   )
    self.assertTrue(np.allclose(__lowerCAmelCase				,				__lowerCAmelCase				,				atol=1E-4   )   )

    __UpperCAmelCase						    =       tf.function(__lowerCAmelCase				,				jit_compile=__lowerCAmelCase   )
    __UpperCAmelCase						    =       tf.math.reduce_mean(xla_generate(inputs.input_ids				,				attention_mask=inputs.attention_mask   )[0]				,				axis=-1   )
    self.assertTrue(np.allclose(__lowerCAmelCase				,				__lowerCAmelCase				,				atol=1E-4   )   )






@require_tf
@slow
class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  @property
  def 	_UpperCAmelCase				(				self:			Tuple   ) -> List[str]:




    '''simple docstring'''




    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:			str   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       "facebook/opt-125m"

    __UpperCAmelCase						    =       [
        "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						    =       []
    __UpperCAmelCase						    =       GPTaTokenizer.from_pretrained(__lowerCAmelCase   )
    __UpperCAmelCase						    =       TFOPTForCausalLM.from_pretrained(__lowerCAmelCase   )

    for prompt in self.prompts:
      __UpperCAmelCase						    =       tokenizer(__lowerCAmelCase				,				return_tensors="tf"   ).input_ids

      __UpperCAmelCase						    =       model.generate(__lowerCAmelCase				,				max_length=10   )

      __UpperCAmelCase						    =       tokenizer.batch_decode(__lowerCAmelCase				,				skip_special_tokens=__lowerCAmelCase   )
      predicted_outputs += generated_string

    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       "facebook/opt-350m"

    __UpperCAmelCase						    =       GPTaTokenizer.from_pretrained(__lowerCAmelCase   )
    __UpperCAmelCase						    =       TFOPTForCausalLM.from_pretrained(__lowerCAmelCase   )

    __UpperCAmelCase						    =       "left"

    # use different length sentences to test batching
    __UpperCAmelCase						    =       [
        "Hello, my dog is a little",
        "Today, I",
    ]

    __UpperCAmelCase						    =       tokenizer(__lowerCAmelCase				,				return_tensors="tf"				,				padding=__lowerCAmelCase   )
    __UpperCAmelCase						    =       inputs["input_ids"]

    __UpperCAmelCase						    =       model.generate(input_ids=__lowerCAmelCase				,				attention_mask=inputs["attention_mask"]   )

    __UpperCAmelCase						    =       tokenizer(sentences[0]				,				return_tensors="tf"   ).input_ids
    __UpperCAmelCase						    =       model.generate(input_ids=__lowerCAmelCase   )

    __UpperCAmelCase						    =       inputs_non_padded.shape[-1] - tf.math.reduce_sum(
        tf.cast(inputs["attention_mask"][-1]				,				tf.intaa   )   )
    __UpperCAmelCase						    =       tokenizer(sentences[1]				,				return_tensors="tf"   ).input_ids
    __UpperCAmelCase						    =       model.generate(input_ids=__lowerCAmelCase				,				max_length=model.config.max_length - num_paddings   )

    __UpperCAmelCase						    =       tokenizer.batch_decode(__lowerCAmelCase				,				skip_special_tokens=__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.decode(output_non_padded[0]				,				skip_special_tokens=__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.decode(output_padded[0]				,				skip_special_tokens=__lowerCAmelCase   )

    __UpperCAmelCase						    =       [
        "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]   )






  def 	_UpperCAmelCase				(				self:			int   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       "facebook/opt-350m"

    __UpperCAmelCase						    =       [
        "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						    =       []
    __UpperCAmelCase						    =       GPTaTokenizer.from_pretrained(__lowerCAmelCase   )
    __UpperCAmelCase						    =       TFOPTForCausalLM.from_pretrained(__lowerCAmelCase   )

    for prompt in self.prompts:
      __UpperCAmelCase						    =       tokenizer(__lowerCAmelCase				,				return_tensors="tf"   ).input_ids

      __UpperCAmelCase						    =       model.generate(__lowerCAmelCase				,				max_length=10   )

      __UpperCAmelCase						    =       tokenizer.batch_decode(__lowerCAmelCase				,				skip_special_tokens=__lowerCAmelCase   )
      predicted_outputs += generated_string

    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_							=      {
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""SqueezeBertTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """SqueezeBertForMaskedLM""",
           """SqueezeBertForMultipleChoice""",
           """SqueezeBertForQuestionAnswering""",
           """SqueezeBertForSequenceClassification""",
           """SqueezeBertForTokenClassification""",
           """SqueezeBertModel""",
           """SqueezeBertModule""",
           """SqueezeBertPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_squeezebert import (
           SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SqueezeBertConfig,
           SqueezeBertOnnxConfig,
       )
       from .tokenization_squeezebert import SqueezeBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_squeezebert import (
                  SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SqueezeBertForMaskedLM,
                  SqueezeBertForMultipleChoice,
                  SqueezeBertForQuestionAnswering,
                  SqueezeBertForSequenceClassification,
                  SqueezeBertForTokenClassification,
                  SqueezeBertModel,
                  SqueezeBertModule,
                  SqueezeBertPreTrainedModel,
              )

else:
       import sys

       a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



