
"""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
      UpperCamelCase      :							List[str]          =					"platform"

      import jax
      import jax.numpy as jnp

      from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def 				A		(							snake_case :Tuple							,   snake_case :List[str]							,   snake_case :Union[str, Any]=None							,   snake_case :Tuple=None						) ->      int:
 if attention_mask is None:
  __UpperCamelCase							    =			np.where(input_ids != config.pad_token_id							,   1							,   0						)
 return {
     "input_ids": input_ids,
     "attention_mask": attention_mask,
 }

@require_flax
class __lowerCAmelCase						:





     def __init__(      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=13      ,	__UpperCAmelCase=7      ,	__UpperCAmelCase=True      ,	__UpperCAmelCase=False      ,	__UpperCAmelCase=99      ,	__UpperCAmelCase=16      ,	__UpperCAmelCase=2      ,	__UpperCAmelCase=4      ,	__UpperCAmelCase=4      ,	__UpperCAmelCase="gelu"      ,	__UpperCAmelCase=0.1      ,	__UpperCAmelCase=0.1      ,	__UpperCAmelCase=20      ,	__UpperCAmelCase=2      ,	__UpperCAmelCase=1      ,	__UpperCAmelCase=0      ,	__UpperCAmelCase=16      ,	__UpperCAmelCase=16      ,	__UpperCAmelCase=0.0_2      ,	):

      '''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							    =			initializer_range
      __UpperCamelCase							    =			False





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,	self.vocab_size )      ,	3      ,	self.vocab_size )
      __UpperCamelCase							    =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,	dtype=np.intaa ))      ,	-1 )

      __UpperCamelCase							    =			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=__UpperCAmelCase      ,	word_embed_proj_dim=self.word_embed_proj_dim      ,	initializer_range=self.initializer_range      ,	use_cache=__UpperCAmelCase      ,	)
      __UpperCamelCase							    =			prepare_opt_inputs_dict(__UpperCAmelCase      ,	__UpperCAmelCase )
      return config, inputs_dict





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.prepare_config_and_inputs()
      return config, inputs_dict





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			20
      __UpperCamelCase							    =			model_class_name(__UpperCAmelCase )

      __UpperCamelCase							    =			inputs_dict['input_ids']
      __UpperCamelCase							    =			inputs_dict['attention_mask']

      __UpperCamelCase							    =			model.init_cache(input_ids.shape[0]      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			jnp.ones((input_ids.shape[0], max_length)      ,	dtype='i4' )

      __UpperCamelCase							    =			jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1 )[None, :]      ,	(input_ids.shape[0], input_ids.shape[-1] - 1)      ,	)
      __UpperCamelCase							    =			model(
          input_ids[:, :-1]      ,	attention_mask=__UpperCAmelCase      ,	past_key_values=__UpperCAmelCase      ,	position_ids=__UpperCAmelCase      ,	)

      __UpperCamelCase							    =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,	dtype='i4' )
      __UpperCamelCase							    =			model(
          input_ids[:, -1:]      ,	attention_mask=__UpperCAmelCase      ,	past_key_values=outputs_cache.past_key_values      ,	position_ids=__UpperCAmelCase      ,	)

      __UpperCamelCase							    =			model(__UpperCAmelCase )

      __UpperCamelCase							    =			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      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			20
      __UpperCamelCase							    =			model_class_name(__UpperCAmelCase )

      __UpperCamelCase    ,      __UpperCamelCase							    =			(
          inputs_dict['input_ids'],
          inputs_dict['attention_mask'],
      )

      __UpperCamelCase							    =			jnp.concatenate(
          [
              attention_mask,
              jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1]) ),
          ]      ,	axis=-1      ,	)

      __UpperCamelCase							    =			model.init_cache(input_ids.shape[0]      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1 )[None, :]      ,	(input_ids.shape[0], input_ids.shape[-1] - 1)      ,	)

      __UpperCamelCase							    =			model(
          input_ids[:, :-1]      ,	attention_mask=__UpperCAmelCase      ,	past_key_values=__UpperCAmelCase      ,	position_ids=__UpperCAmelCase      ,	)
      __UpperCamelCase							    =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,	dtype='i4' )
      __UpperCamelCase							    =			model(
          input_ids[:, -1:]      ,	past_key_values=outputs_cache.past_key_values      ,	attention_mask=__UpperCAmelCase      ,	position_ids=__UpperCAmelCase      ,	)

      __UpperCamelCase							    =			model(__UpperCAmelCase      ,	attention_mask=__UpperCAmelCase )

      __UpperCamelCase							    =			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 __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ,    __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
     lowercase		  =		() if is_flax_available() else ()





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			FlaxOPTModelTester(self )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			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(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )







     @slow
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      for model_class_name in self.all_model_classes:
       __UpperCamelCase							    =			model_class_name.from_pretrained('facebook/opt-125m' )
       __UpperCamelCase							    =			np.ones((1, 1) ) * model.config.eos_token_id
       __UpperCamelCase							    =			model(__UpperCAmelCase )
       self.assertIsNotNone(__UpperCAmelCase )


@require_sentencepiece
@require_flax
class __lowerCAmelCase						( unittest.TestCase     ):





     @slow
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			FlaxOPTModel.from_pretrained('facebook/opt-350m' )
      __UpperCamelCase							    =			jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
      __UpperCamelCase							    =			model(input_ids=__UpperCAmelCase ).last_hidden_state
      __UpperCamelCase							    =			(1, 11, 512)
      self.assertEqual(output.shape      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			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]      ,	__UpperCAmelCase      ,	atol=4E-2 ) )


@require_flax
@slow
class __lowerCAmelCase						( unittest.TestCase     ):





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      super().setUp()
      __UpperCamelCase							    =			'facebook/opt-350m'







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			FlaxOPTForCausalLM.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(__UpperCAmelCase      ,	return_tensors='jax'      ,	padding=__UpperCAmelCase      ,	add_special_tokens=__UpperCAmelCase )
      __UpperCamelCase							    =			model(inputs.input_ids      ,	attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
      __UpperCamelCase							    =			jnp.array(
          [
              [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
              [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -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, -1_4.1_6_5_0, 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, -1_0.7_9_2_6, -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(__UpperCAmelCase      ,	__UpperCAmelCase      ,	atol=4E-2 ) )

      __UpperCamelCase							    =			jax.jit(__UpperCAmelCase )
      __UpperCamelCase							    =			model(inputs.input_ids      ,	attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
      self.assertTrue(jnp.allclose(__UpperCAmelCase      ,	__UpperCAmelCase      ,	atol=4E-2 ) )


@require_flax
@slow
class __lowerCAmelCase						( unittest.TestCase     ):





     @property
     def 		UpperCAmelCase   (      self ):

      '''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 ):

      '''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							    =			FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase )
      __UpperCamelCase							    =			GPTaTokenizer.from_pretrained(__UpperCAmelCase )

      for prompt in self.prompts:
       __UpperCamelCase							    =			tokenizer(__UpperCAmelCase      ,	return_tensors='jax' ).input_ids

       __UpperCamelCase							    =			model.generate(__UpperCAmelCase      ,	max_length=10 )
       __UpperCamelCase							    =			generated_ids[0]

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

      self.assertListEqual(__UpperCAmelCase      ,	__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''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							    =			FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase )
      __UpperCamelCase							    =			GPTaTokenizer.from_pretrained(__UpperCAmelCase )

      for prompt in self.prompts:
       __UpperCamelCase							    =			tokenizer(__UpperCAmelCase      ,	return_tensors='jax' ).input_ids

       __UpperCamelCase							    =			model.generate(__UpperCAmelCase      ,	max_length=10 )
       __UpperCamelCase							    =			generated_ids[0]

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

      self.assertListEqual(__UpperCAmelCase      ,	__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			'facebook/opt-125m'
      __UpperCamelCase							    =			[
          'Today is a beautiful day and I want to thank',
          'In the city of Rome Canaver Canaver Canaver Canaver',
      ]
      __UpperCamelCase							    =			FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase )
      __UpperCamelCase							    =			GPTaTokenizer.from_pretrained(__UpperCAmelCase )
      __UpperCamelCase							    =			tokenizer(
          [
              'Today is a beautiful day and I want to',
              'In the city of',
          ]      ,	return_tensors='jax'      ,	padding=__UpperCAmelCase      ,	)

      __UpperCamelCase							    =			jax.jit(model.generate )

      __UpperCamelCase							    =			jit_generate(inputs['input_ids']      ,	attention_mask=inputs['attention_mask'] ).sequences

      __UpperCamelCase							    =			tokenizer.batch_decode(__UpperCAmelCase      ,	skip_special_tokens=__UpperCAmelCase )

      self.assertIsNotNone(__UpperCAmelCase      ,	__UpperCAmelCase )







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			'facebook/opt-350m'

      __UpperCamelCase							    =			GPTaTokenizer.from_pretrained(__UpperCAmelCase )
      __UpperCamelCase							    =			FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase )

      __UpperCamelCase							    =			'left'

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

      __UpperCamelCase							    =			tokenizer(__UpperCAmelCase      ,	return_tensors='jax'      ,	padding=__UpperCAmelCase )
      __UpperCamelCase							    =			inputs['input_ids']

      __UpperCamelCase							    =			model.generate(input_ids=__UpperCAmelCase      ,	attention_mask=inputs['attention_mask']      ,	trace=__UpperCAmelCase )

      __UpperCamelCase							    =			tokenizer(sentences[0]      ,	return_tensors='jax' ).input_ids
      __UpperCamelCase							    =			model.generate(input_ids=__UpperCAmelCase )

      __UpperCamelCase							    =			inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
      __UpperCamelCase							    =			tokenizer(sentences[1]      ,	return_tensors='jax' ).input_ids
      __UpperCamelCase							    =			model.generate(input_ids=__UpperCAmelCase      ,	max_length=model.config.max_length - num_paddings )

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

      __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(__UpperCAmelCase      ,	__UpperCAmelCase )
      self.assertListEqual(__UpperCAmelCase      ,	[non_padded_sentence, padded_sentence] )





"""simple docstring"""

import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def 				A		(							snake_case :Dict							,   snake_case :int							,   snake_case :List[str]							,   snake_case :int							,   snake_case :List[str]						) ->      List[str]:
 # Initialise PyTorch model
 __UpperCamelCase							    =			BertConfig.from_pretrained(
     'bert-large-cased'							,   vocab_size=snake_case							,   max_position_embeddings=5_1_2							,   is_decoder=snake_case							,   add_cross_attention=snake_case							,   )
 __UpperCamelCase							    =			bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 __UpperCamelCase							    =			BertGenerationConfig(**snake_case						)
 if is_encoder:
  __UpperCamelCase							    =			BertGenerationEncoder(snake_case						)
 else:
  __UpperCamelCase							    =			BertGenerationDecoder(snake_case						)
 print(f'Building PyTorch model from configuration: {config}'						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     snake_case							,   snake_case							,   model_class='bert'							,   is_encoder_named_decoder=snake_case							,   is_encoder=snake_case							,   )

 # Save pytorch-model
 print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
 model.save_pretrained(snake_case						)


if __name__ == "__main__":
      UpperCamelCase      :							List[str]          =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_named_decoder",
          action="store_true",
          help="If decoder has to be renamed to encoder in PyTorch model.",
      )
      parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
      parser.add_argument("--vocab_size", default=5_0_3_5_8, type=int, help="Vocab size of model")
      UpperCamelCase      :							Dict          =					parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )



