




"""simple docstring"""



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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_=None      ,			lowercase_=None						)						->		List[str]:
 if attention_mask is None:
  A__	=    tf.cast(tf.math.not_equal(lowercase_      ,			config.pad_token_id						)      ,			tf.inta						)
 return {"input_ids": input_ids, "attention_mask": attention_mask}




@require_tf
class      UpperCAmelCase_  :
    lowercase__						 =				OPTConfig
    lowercase__						 =				{}
    lowercase__						 =				'''gelu'''




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




     '''simple docstring'''

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




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




     '''simple docstring'''

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

     A__	=    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=snake_case_     ,			**self.config_updates     ,			)
     A__	=    prepare_opt_inputs_dict(snake_case_     ,			snake_case_				)
     return config, inputs_dict




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Optional[int]				)				->							Dict:




     '''simple docstring'''

     A__	=    TFOPTModel(config=snake_case_				)
     A__	=    inputs_dict["input_ids"]

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

     # first forward pass
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			use_cache=snake_case_				)

     A__,				A__	=    outputs.to_tuple()

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

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

     A__	=    model(snake_case_     ,			attention_mask=snake_case_				)[0]
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			past_key_values=snake_case_				)[0]

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

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

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






@require_tf
class      UpperCAmelCase_  (						A_,       A_,       unittest.TestCase							):
    lowercase__						 =				(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
    lowercase__						 =				(TFOPTForCausalLM,) if is_tf_available() else ()
    lowercase__						 =				(
        {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
    )
    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				10




    def 							__magic_name__							(   self						:	Dict				)				->							str:




     '''simple docstring'''

     A__	=    TFOPTModelTester(self				)
     A__	=    ConfigTester(self     ,			config_class=snake_case_				)




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




     '''simple docstring'''

     self.config_tester.run_common_tests()




    def 							__magic_name__							(   self						:	Any				)				->							List[Any]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*snake_case_				)




    def 							__magic_name__							(   self						:	Tuple				)				->							Dict:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

     def _get_word_embedding_weight(snake_case_						:	str     ,			snake_case_						:	List[str]				):
      if hasattr(snake_case_     ,			"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(snake_case_     ,			"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
       A__	=    model_class(config=snake_case_				)
       A__	=    _get_word_embedding_weight(snake_case_     ,			model.get_input_embeddings()				)
       A__	=    _get_word_embedding_weight(snake_case_     ,			model.get_output_embeddings()				)

       # reshape the embeddings
       model.resize_token_embeddings(snake_case_				)
       A__	=    _get_word_embedding_weight(snake_case_     ,			model.get_input_embeddings()				)
       A__	=    _get_word_embedding_weight(snake_case_     ,			model.get_output_embeddings()				)

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

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

       # check that weights remain the same after resizing
       A__	=    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:
         A__	=    False
       self.assertTrue(snake_case_				)

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

        A__	=    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:
          A__	=    False
        self.assertTrue(snake_case_				)







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Union[str, Any]:
 return tf.constant(lowercase_      ,			dtype=tf.intaa						)




@require_tf
class      UpperCAmelCase_  (						unittest.TestCase							):
    lowercase__						 =				99




    def 							__magic_name__							(   self						:	Tuple				)				->							str:




     '''simple docstring'''

     A__	=    tf.ones((4, 1)     ,			dtype=tf.intaa				) * 2
     A__	=    tf.concat([ids_tensor((4, 6)     ,			self.vocab_size - 3				) + 3, eos_column_vector]     ,			axis=1				)
     A__	=    input_ids.shape[0]
     A__	=    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							):




    @slow
    def 							__magic_name__							(   self						:	int				)				->							List[str]:




     '''simple docstring'''

     A__	=    TFOPTModel.from_pretrained("facebook/opt-350m"				)
     A__	=    _long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]				)
     A__	=    tf.not_equal(snake_case_     ,			model.config.pad_token_id				)
     with tf.GradientTape():
      A__	=    model(input_ids=snake_case_     ,			attention_mask=snake_case_				).last_hidden_state
     A__	=    (1, 11, 512)
     self.assertEqual(output.shape     ,			snake_case_				)
     A__	=    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]     ,			snake_case_     ,			atol=4e-3				)				)

     A__	=    tf.function(snake_case_     ,			jit_compile=snake_case_				)
     A__	=    xla_generate(snake_case_     ,			snake_case_				)[0]
     self.assertTrue(np.allclose(output[:, :3, :3]     ,			snake_case_     ,			atol=4e-2				)				)






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




    def 							__magic_name__							(   self						:	Any				)				->							int:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Optional[int]				)				->							Union[str, Any]:




     '''simple docstring'''

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

     A__	=    [
         "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__	=    tokenizer(snake_case_     ,			return_tensors="tf"     ,			padding=snake_case_     ,			add_special_tokens=snake_case_				)
     A__	=    tf.math.reduce_mean(model(inputs.input_ids     ,			attention_mask=inputs.attention_mask				)[0]     ,			axis=-1				)
     A__	=    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(snake_case_     ,			snake_case_     ,			atol=1e-4				)				)

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






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




    @property
    def 							__magic_name__							(   self						:	Optional[int]				)				->							Tuple:




     '''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 							__magic_name__							(   self						:	List[Any]				)				->							Dict:




     '''simple docstring'''

     A__	=    "facebook/opt-125m"

     A__	=    [
         "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__	=    []
     A__	=    GPTaTokenizer.from_pretrained(snake_case_				)
     A__	=    TFOPTForCausalLM.from_pretrained(snake_case_				)

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

      A__	=    model.generate(snake_case_     ,			max_length=10				)

      A__	=    tokenizer.batch_decode(snake_case_     ,			skip_special_tokens=snake_case_				)
      predicted_outputs += generated_string

     self.assertListEqual(snake_case_     ,			snake_case_				)




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




     '''simple docstring'''

     A__	=    "facebook/opt-350m"

     A__	=    GPTaTokenizer.from_pretrained(snake_case_				)
     A__	=    TFOPTForCausalLM.from_pretrained(snake_case_				)

     A__	=    "left"

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

     A__	=    tokenizer(snake_case_     ,			return_tensors="tf"     ,			padding=snake_case_				)
     A__	=    inputs["input_ids"]

     A__	=    model.generate(input_ids=snake_case_     ,			attention_mask=inputs["attention_mask"]				)

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

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

     A__	=    tokenizer.batch_decode(snake_case_     ,			skip_special_tokens=snake_case_				)
     A__	=    tokenizer.decode(output_non_padded[0]     ,			skip_special_tokens=snake_case_				)
     A__	=    tokenizer.decode(output_padded[0]     ,			skip_special_tokens=snake_case_				)

     A__	=    [
         "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(snake_case_     ,			snake_case_				)
     self.assertListEqual(snake_case_     ,			[non_padded_sentence, padded_sentence]				)




    def 							__magic_name__							(   self						:	int				)				->							Dict:




     '''simple docstring'''

     A__	=    "facebook/opt-350m"

     A__	=    [
         "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__	=    []
     A__	=    GPTaTokenizer.from_pretrained(snake_case_				)
     A__	=    TFOPTForCausalLM.from_pretrained(snake_case_				)

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

      A__	=    model.generate(snake_case_     ,			max_length=10				)

      A__	=    tokenizer.batch_decode(snake_case_     ,			skip_special_tokens=snake_case_				)
      predicted_outputs += generated_string

     self.assertListEqual(snake_case_     ,			snake_case_				)











"""simple docstring"""



from argparse import ArgumentParser

from .env import EnvironmentCommand







def   _SCREAMING_SNAKE_CASE       (							)						->		List[Any]:
 A__	=    ArgumentParser("Diffusers CLI tool"      ,			usage="diffusers-cli <command> [<args>]"						)
 A__	=    parser.add_subparsers(help="diffusers-cli command helpers"						)

 # Register commands
 EnvironmentCommand.register_subcommand(lowercase_						)

 # Let's go
 A__	=    parser.parse_args()

 if not hasattr(lowercase_      ,			"func"						):
  parser.print_help()
  exit(1						)

 # Run
 A__	=    args.func(lowercase_						)
 service.run()


if __name__ == "__main__":
      main()





