





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       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None)       ->   Any:
  if attention_mask is None:
    __snake_case:						Tuple			  =						tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE__ , config.pad_token_id) , tf.inta)
  return {"input_ids": input_ids, "attention_mask": attention_mask}







@require_tf
class    __snake_case	:





       '''simple docstring'''







       lowerCAmelCase__						=					OPTConfig
       lowerCAmelCase__						=					{}
       lowerCAmelCase__						=					"""gelu"""




       def __init__(  self			:   Tuple							,    A			:   Any							,    A			:   Optional[Any]=13							,    A			:   List[str]=7							,    A			:   List[str]=True							,    A			:   Optional[Any]=False							,    A			:   Dict=99							,    A			:   List[Any]=16							,    A			:   Union[str, Any]=2							,    A			:   str=4							,    A			:   Tuple=4							,    A			:   Union[str, Any]="gelu"							,    A			:   Tuple=0.1							,    A			:   List[Any]=0.1							,    A			:   str=20							,    A			:   List[str]=2							,    A			:   Any=1							,    A			:   Tuple=0							,    A			:   List[str]=16							,    A			:   Tuple=16							,    ):
         __snake_case:						Tuple			  =						parent
         __snake_case:						int			  =						batch_size
         __snake_case:						Optional[int]			  =						seq_length
         __snake_case:						Optional[int]			  =						is_training
         __snake_case:						Union[str, Any]			  =						use_labels
         __snake_case:						List[str]			  =						vocab_size
         __snake_case:						Any			  =						hidden_size
         __snake_case:						int			  =						num_hidden_layers
         __snake_case:						str			  =						num_attention_heads
         __snake_case:						List[Any]			  =						intermediate_size
         __snake_case:						Union[str, Any]			  =						hidden_act
         __snake_case:						Tuple			  =						hidden_dropout_prob
         __snake_case:						Dict			  =						attention_probs_dropout_prob
         __snake_case:						str			  =						max_position_embeddings
         __snake_case:						Optional[int]			  =						eos_token_id
         __snake_case:						List[Any]			  =						pad_token_id
         __snake_case:						Tuple			  =						bos_token_id
         __snake_case:						Tuple			  =						embed_dim
         __snake_case:						Union[str, Any]			  =						word_embed_proj_dim
         __snake_case:						List[str]			  =						False




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Any			  =						ids_tensor([self.batch_size, self.seq_length - 1]							,    self.vocab_size		)
         __snake_case:						Optional[Any]			  =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)							,    1		)
         __snake_case:						Optional[Any]			  =						tf.concat([input_ids, eos_tensor]							,    axis=1		)

         __snake_case:						Dict			  =						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=A							,    **self.config_updates							,    )
         __snake_case:						Dict			  =						prepare_opt_inputs_dict(A							,    A		)
         return config, inputs_dict






       def 							UpperCAmelCase__			(  self			:   Tuple							,    A			:   Optional[Any]							,    A			:   Optional[Any]		):
         __snake_case:						Dict			  =						TFOPTModel(config=A		)
         __snake_case:						List[str]			  =						inputs_dict["""input_ids"""]

         __snake_case:						Dict			  =						input_ids[:1, :]
         __snake_case:						int			  =						inputs_dict["""attention_mask"""][:1, :]
         __snake_case:						List[Any]			  =						1

         # first forward pass
         __snake_case:						Tuple			  =						model(A							,    attention_mask=A							,    use_cache=A		)

         __snake_case:						Optional[int]			  =						outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         __snake_case:						Union[str, Any]			  =						ids_tensor((self.batch_size, 3)							,    config.vocab_size		)
         __snake_case:						Optional[Any]			  =						tf.cast(ids_tensor((self.batch_size, 3)							,    2		)							,    tf.inta		)

         # append to next input_ids and
         __snake_case:						Tuple			  =						tf.concat([input_ids, next_tokens]							,    axis=-1		)
         __snake_case:						List[Any]			  =						tf.concat([attention_mask, next_attn_mask]							,    axis=-1		)

         __snake_case:						Dict			  =						model(A							,    attention_mask=A		)[0]
         __snake_case:						Tuple			  =						model(A							,    attention_mask=A							,    past_key_values=A		)[0]

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

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

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







@require_tf
class    __snake_case	(							__lowerCamelCase       ,      __lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
       lowerCAmelCase__						=					(TFOPTForCausalLM,) if is_tf_available() else ()
       lowerCAmelCase__						=					(
           {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
       )
       lowerCAmelCase__						=					False
       lowerCAmelCase__						=					False
       lowerCAmelCase__						=					False
       lowerCAmelCase__						=					10




       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case:						Dict			  =						TFOPTModelTester(self		)
         __snake_case:						Dict			  =						ConfigTester(self							,    config_class=A		)




       def 							UpperCAmelCase__			(  self			:   Tuple		):
         self.config_tester.run_common_tests()




       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         __snake_case:						Dict			  =						self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*A		)






       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         __snake_case:						Optional[Any]			  =						self.model_tester.prepare_config_and_inputs_for_common()

         def _get_word_embedding_weight(A			:   Tuple							,    A			:   Any		):
           if hasattr(A							,    """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(A							,    """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
             __snake_case:						Optional[Any]			  =						model_class(config=A		)
             __snake_case:						List[str]			  =						_get_word_embedding_weight(A							,    model.get_input_embeddings()		)
             __snake_case:						Optional[Any]			  =						_get_word_embedding_weight(A							,    model.get_output_embeddings()		)

             # reshape the embeddings
             model.resize_token_embeddings(A		)
             __snake_case:						Optional[int]			  =						_get_word_embedding_weight(A							,    model.get_input_embeddings()		)
             __snake_case:						Dict			  =						_get_word_embedding_weight(A							,    model.get_output_embeddings()		)

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

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

             # check that weights remain the same after resizing
             __snake_case:						str			  =						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:
                 __snake_case:						int			  =						False
             self.assertTrue(A		)

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

               __snake_case:						List[str]			  =						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:
                   __snake_case:						List[Any]			  =						False
               self.assertTrue(A		)







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   Optional[int]:
  return tf.constant(SCREAMING_SNAKE_CASE__ , dtype=tf.intaa)







@require_tf
class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					99




       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         __snake_case:						List[str]			  =						tf.ones((4, 1)							,    dtype=tf.intaa		) * 2
         __snake_case:						Tuple			  =						tf.concat([ids_tensor((4, 6)							,    self.vocab_size - 3		) + 3, eos_column_vector]							,    axis=1		)
         __snake_case:						List[str]			  =						input_ids.shape[0]
         __snake_case:						Optional[int]			  =						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    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       @slow
       def 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						Dict			  =						TFOPTModel.from_pretrained("""facebook/opt-350m"""		)
         __snake_case:						int			  =						_long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]		)
         __snake_case:						Tuple			  =						tf.not_equal(A							,    model.config.pad_token_id		)
         with tf.GradientTape():
           __snake_case:						int			  =						model(input_ids=A							,    attention_mask=A		).last_hidden_state
         __snake_case:						Optional[int]			  =						(1, 11, 512)
         self.assertEqual(output.shape							,    A		)
         __snake_case:						int			  =						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]							,    A							,    atol=4E-3		)		)

         __snake_case:						Dict			  =						tf.function(A							,    jit_compile=A		)
         __snake_case:						Optional[int]			  =						xla_generate(A							,    A		)[0]
         self.assertTrue(np.allclose(output[:, :3, :3]							,    A							,    atol=4E-2		)		)







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





       '''simple docstring'''







       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         super().setUp()
         __snake_case:						int			  =						"""facebook/opt-350m"""






       def 							UpperCAmelCase__			(  self			:   List[str]		):
         __snake_case:						int			  =						TFOPTForCausalLM.from_pretrained(self.path_model		)
         __snake_case:						Union[str, Any]			  =						GPTaTokenizer.from_pretrained(self.path_model		)

         __snake_case:						Dict			  =						[
             """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
         __snake_case:						List[str]			  =						tokenizer(A							,    return_tensors="""tf"""							,    padding=A							,    add_special_tokens=A		)
         __snake_case:						Union[str, Any]			  =						tf.math.reduce_mean(model(inputs.input_ids							,    attention_mask=inputs.attention_mask		)[0]							,    axis=-1		)
         __snake_case:						Union[str, Any]			  =						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(A							,    A							,    atol=1E-4		)		)

         __snake_case:						List[Any]			  =						tf.function(A							,    jit_compile=A		)
         __snake_case:						int			  =						tf.math.reduce_mean(xla_generate(inputs.input_ids							,    attention_mask=inputs.attention_mask		)[0]							,    axis=-1		)
         self.assertTrue(np.allclose(A							,    A							,    atol=1E-4		)		)







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





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__			(  self			:   int		):
         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			:   List[str]		):
         __snake_case:						str			  =						"""facebook/opt-125m"""

         __snake_case:						int			  =						[
             """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""",
         ]

         __snake_case:						Any			  =						[]
         __snake_case:						List[Any]			  =						GPTaTokenizer.from_pretrained(A		)
         __snake_case:						Optional[Any]			  =						TFOPTForCausalLM.from_pretrained(A		)

         for prompt in self.prompts:
           __snake_case:						Optional[int]			  =						tokenizer(A							,    return_tensors="""tf"""		).input_ids

           __snake_case:						Tuple			  =						model.generate(A							,    max_length=10		)

           __snake_case:						Optional[int]			  =						tokenizer.batch_decode(A							,    skip_special_tokens=A		)
           predicted_outputs += generated_string

         self.assertListEqual(A							,    A		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Tuple			  =						"""facebook/opt-350m"""

         __snake_case:						int			  =						GPTaTokenizer.from_pretrained(A		)
         __snake_case:						Tuple			  =						TFOPTForCausalLM.from_pretrained(A		)

         __snake_case:						Tuple			  =						"""left"""

         # use different length sentences to test batching
         __snake_case:						Union[str, Any]			  =						[
             """Hello, my dog is a little""",
             """Today, I""",
         ]

         __snake_case:						Tuple			  =						tokenizer(A							,    return_tensors="""tf"""							,    padding=A		)
         __snake_case:						Dict			  =						inputs["""input_ids"""]

         __snake_case:						Optional[int]			  =						model.generate(input_ids=A							,    attention_mask=inputs["""attention_mask"""]		)

         __snake_case:						Optional[Any]			  =						tokenizer(sentences[0]							,    return_tensors="""tf"""		).input_ids
         __snake_case:						str			  =						model.generate(input_ids=A		)

         __snake_case:						Union[str, Any]			  =						inputs_non_padded.shape[-1] - tf.math.reduce_sum(
             tf.cast(inputs["""attention_mask"""][-1]							,    tf.intaa		)		)
         __snake_case:						List[Any]			  =						tokenizer(sentences[1]							,    return_tensors="""tf"""		).input_ids
         __snake_case:						List[Any]			  =						model.generate(input_ids=A							,    max_length=model.config.max_length - num_paddings		)

         __snake_case:						Union[str, Any]			  =						tokenizer.batch_decode(A							,    skip_special_tokens=A		)
         __snake_case:						Optional[Any]			  =						tokenizer.decode(output_non_padded[0]							,    skip_special_tokens=A		)
         __snake_case:						Union[str, Any]			  =						tokenizer.decode(output_padded[0]							,    skip_special_tokens=A		)

         __snake_case:						List[str]			  =						[
             """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(A							,    A		)
         self.assertListEqual(A							,    [non_padded_sentence, padded_sentence]		)






       def 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						List[str]			  =						"""facebook/opt-350m"""

         __snake_case:						str			  =						[
             """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""",
         ]

         __snake_case:						str			  =						[]
         __snake_case:						Dict			  =						GPTaTokenizer.from_pretrained(A		)
         __snake_case:						List[str]			  =						TFOPTForCausalLM.from_pretrained(A		)

         for prompt in self.prompts:
           __snake_case:						Tuple			  =						tokenizer(A							,    return_tensors="""tf"""		).input_ids

           __snake_case:						Optional[Any]			  =						model.generate(A							,    max_length=10		)

           __snake_case:						Any			  =						tokenizer.batch_decode(A							,    skip_special_tokens=A		)
           predicted_outputs += generated_string

         self.assertListEqual(A							,    A		)









from __future__ import annotations

import numpy as np







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   List[str]:
  return np.maximum(0 , SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]






