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  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       List[Any]=None ,     __UpperCamelCase    :       Tuple=None	)							->					Dict:







  """simple docstring"""





  if attention_mask is None:
    SCREAMING_SNAKE_CASE__										=      tf.cast(tf.math.not_equal(__UpperCamelCase ,     config.pad_token_id	) ,     tf.inta	)
  return {"input_ids": input_ids, "attention_mask": attention_mask}







@require_tf
class __snake_case				:
   lowerCAmelCase_														=							OPTConfig
   lowerCAmelCase_														=							{}
   lowerCAmelCase_														=							"gelu"




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



     """simple docstring"""






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




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      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=_lowercase       ,	**self.config_updates       ,	)
     SCREAMING_SNAKE_CASE__										=      prepare_opt_inputs_dict(_lowercase       ,	_lowercase      )
     return config, inputs_dict




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Dict       ,	_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      TFOPTModel(config=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      inputs_dict["""input_ids"""]

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

     # first forward pass
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase       ,	use_cache=_lowercase      )

     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      outputs.to_tuple()

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

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

     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase      )[0]
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase       ,	past_key_values=_lowercase      )[0]

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

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

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







@require_tf
class __snake_case				(						lowerCamelCase_   ,		lowerCamelCase_   ,		unittest.TestCase		):
   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 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      TFOPTModelTester(self      )
     SCREAMING_SNAKE_CASE__										=      ConfigTester(self       ,	config_class=_lowercase      )




   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*_lowercase      )




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_common()

     def _get_word_embedding_weight(_lowercase			:				int       ,	_lowercase			:				Union[str, Any]      ):
       if hasattr(_lowercase       ,	"""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(_lowercase       ,	"""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
         SCREAMING_SNAKE_CASE__										=      model_class(config=_lowercase      )
         SCREAMING_SNAKE_CASE__										=      _get_word_embedding_weight(_lowercase       ,	model.get_input_embeddings()      )
         SCREAMING_SNAKE_CASE__										=      _get_word_embedding_weight(_lowercase       ,	model.get_output_embeddings()      )

         # reshape the embeddings
         model.resize_token_embeddings(_lowercase      )
         SCREAMING_SNAKE_CASE__										=      _get_word_embedding_weight(_lowercase       ,	model.get_input_embeddings()      )
         SCREAMING_SNAKE_CASE__										=      _get_word_embedding_weight(_lowercase       ,	model.get_output_embeddings()      )

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

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

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

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

           SCREAMING_SNAKE_CASE__										=      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:
               SCREAMING_SNAKE_CASE__										=      False
           self.assertTrue(_lowercase      )





def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str	)							->					Optional[Any]:







  """simple docstring"""





  return tf.constant(__UpperCamelCase ,     dtype=tf.intaa	)







@require_tf
class __snake_case				(						unittest.TestCase		):
   lowerCAmelCase_														=							99




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






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




   @slow
   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      TFOPTModel.from_pretrained("""facebook/opt-350m"""      )
     SCREAMING_SNAKE_CASE__										=      _long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]      )
     SCREAMING_SNAKE_CASE__										=      tf.not_equal(_lowercase       ,	model.config.pad_token_id      )
     with tf.GradientTape():
       SCREAMING_SNAKE_CASE__										=      model(input_ids=_lowercase       ,	attention_mask=_lowercase      ).last_hidden_state
     SCREAMING_SNAKE_CASE__										=      (1, 11, 5_12)
     self.assertEqual(output.shape       ,	_lowercase      )
     SCREAMING_SNAKE_CASE__										=      tf.constant(
         [[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]]      )
     self.assertTrue(np.allclose(output[:, :3, :3]       ,	_lowercase       ,	atol=4E-3      )      )

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







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




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				int      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      [
         """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
     SCREAMING_SNAKE_CASE__										=      tokenizer(_lowercase       ,	return_tensors="""tf"""       ,	padding=_lowercase       ,	add_special_tokens=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      tf.math.reduce_mean(model(inputs.input_ids       ,	attention_mask=inputs.attention_mask      )[0]       ,	axis=-1      )
     SCREAMING_SNAKE_CASE__										=      tf.constant(
         [
             [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
             [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
             [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
             [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
         ]      )
     self.assertTrue(np.allclose(_lowercase       ,	_lowercase       ,	atol=1E-4      )      )

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







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




   @property
   def 	__a						(    self			:				List[Any]      ):



     """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 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """facebook/opt-125m"""

     SCREAMING_SNAKE_CASE__										=      [
         """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""",
     ]

     SCREAMING_SNAKE_CASE__										=      []
     SCREAMING_SNAKE_CASE__										=      GPTaTokenizer.from_pretrained(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      TFOPTForCausalLM.from_pretrained(_lowercase      )

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

       SCREAMING_SNAKE_CASE__										=      model.generate(_lowercase       ,	max_length=10      )

       SCREAMING_SNAKE_CASE__										=      tokenizer.batch_decode(_lowercase       ,	skip_special_tokens=_lowercase      )
       predicted_outputs += generated_string

     self.assertListEqual(_lowercase       ,	_lowercase      )




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """facebook/opt-350m"""

     SCREAMING_SNAKE_CASE__										=      GPTaTokenizer.from_pretrained(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      TFOPTForCausalLM.from_pretrained(_lowercase      )

     SCREAMING_SNAKE_CASE__										=      """left"""

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

     SCREAMING_SNAKE_CASE__										=      tokenizer(_lowercase       ,	return_tensors="""tf"""       ,	padding=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      inputs["""input_ids"""]

     SCREAMING_SNAKE_CASE__										=      model.generate(input_ids=_lowercase       ,	attention_mask=inputs["""attention_mask"""]      )

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

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

     SCREAMING_SNAKE_CASE__										=      tokenizer.batch_decode(_lowercase       ,	skip_special_tokens=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      tokenizer.decode(output_non_padded[0]       ,	skip_special_tokens=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      tokenizer.decode(output_padded[0]       ,	skip_special_tokens=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      [
         """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(_lowercase       ,	_lowercase      )
     self.assertListEqual(_lowercase       ,	[non_padded_sentence, padded_sentence]      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """facebook/opt-350m"""

     SCREAMING_SNAKE_CASE__										=      [
         """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""",
     ]

     SCREAMING_SNAKE_CASE__										=      []
     SCREAMING_SNAKE_CASE__										=      GPTaTokenizer.from_pretrained(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      TFOPTForCausalLM.from_pretrained(_lowercase      )

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

       SCREAMING_SNAKE_CASE__										=      model.generate(_lowercase       ,	max_length=10      )

       SCREAMING_SNAKE_CASE__										=      tokenizer.batch_decode(_lowercase       ,	skip_special_tokens=_lowercase      )
       predicted_outputs += generated_string

     self.assertListEqual(_lowercase       ,	_lowercase      )

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict







@dataclass
class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							ComputeEnvironment.AMAZON_SAGEMAKER
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							"ml.p3.2xlarge"
   lowerCAmelCase_														=							"accelerate_sagemaker_execution_role"
   lowerCAmelCase_														=							"hf-sm"
   lowerCAmelCase_														=							"us-east-1"
   lowerCAmelCase_														=							1
   lowerCAmelCase_														=							"accelerate-sagemaker-1"
   lowerCAmelCase_														=							"1.6"
   lowerCAmelCase_														=							"4.4"
   lowerCAmelCase_														=							"train.py"
   lowerCAmelCase_														=							[
       "--model_name_or_path",
       "bert",
       "--do_train",
       "False",
       "--epochs",
       "3",
       "--learning_rate",
       "5e-5",
       "--max_steps",
       "50.5",
   ]
   lowerCAmelCase_														=							[
       "--model_name_or_path",
       "bert",
       "--do_train",
       "--do_test",
       "False",
       "--do_predict",
       "--epochs",
       "3",
       "--learning_rate",
       "5e-5",
       "--max_steps",
       "50.5",
   ]







class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args      )
     assert isinstance(converted_args["""model_name_or_path"""]       ,	_lowercase      )
     assert isinstance(converted_args["""do_train"""]       ,	_lowercase      )
     assert isinstance(converted_args["""epochs"""]       ,	_lowercase      )
     assert isinstance(converted_args["""learning_rate"""]       ,	_lowercase      )
     assert isinstance(converted_args["""max_steps"""]       ,	_lowercase      )

     with pytest.raises(_lowercase      ):
       _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args      )
