
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
				import jax
				import jax.numpy as jnp

				from transformers.modeling_flax_pytorch_utils import (
				    convert_pytorch_state_dict_to_flax,
				    load_flax_weights_in_pytorch_model,
				)
				from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
				import torch






class 				SCREAMING_SNAKE_CASE__			:

							"""simple docstring"""






							def __init__(     self   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_=14   ,		SCREAMING_SNAKE_CASE_=7   ,		SCREAMING_SNAKE_CASE_=True   ,		SCREAMING_SNAKE_CASE_=True   ,		SCREAMING_SNAKE_CASE_=False   ,		SCREAMING_SNAKE_CASE_=True   ,		SCREAMING_SNAKE_CASE_=99   ,		SCREAMING_SNAKE_CASE_=32   ,		SCREAMING_SNAKE_CASE_=4   ,		SCREAMING_SNAKE_CASE_=4   ,		SCREAMING_SNAKE_CASE_=4   ,		SCREAMING_SNAKE_CASE_=37   ,		SCREAMING_SNAKE_CASE_="gelu"   ,		SCREAMING_SNAKE_CASE_=0.1   ,		SCREAMING_SNAKE_CASE_=0.1   ,		SCREAMING_SNAKE_CASE_=512   ,		SCREAMING_SNAKE_CASE_=0.0_2   ,		)->      Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase        =	parent
													__UpperCamelCase        =	batch_size
													__UpperCamelCase        =	seq_length
													__UpperCamelCase        =	is_training
													__UpperCamelCase        =	use_input_mask
													__UpperCamelCase        =	use_token_type_ids
													__UpperCamelCase        =	use_labels
													__UpperCamelCase        =	vocab_size
													__UpperCamelCase        =	hidden_size
													__UpperCamelCase        =	rotary_dim
													__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        =	initializer_range
													__UpperCamelCase        =	None
													__UpperCamelCase        =	vocab_size - 1
													__UpperCamelCase        =	vocab_size - 1
													__UpperCamelCase        =	vocab_size - 1



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





													'''simple docstring'''






													__UpperCamelCase        =	ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size	)

													__UpperCamelCase        =	None
													if self.use_input_mask:
																			__UpperCamelCase        =	random_attention_mask([self.batch_size, self.seq_length]	)

													__UpperCamelCase        =	GPTJConfig(
													    vocab_size=self.vocab_size   ,		n_embd=self.hidden_size   ,		n_layer=self.num_hidden_layers   ,		n_head=self.num_attention_heads   ,		n_positions=self.max_position_embeddings   ,		use_cache=__UpperCAmelCase   ,		bos_token_id=self.bos_token_id   ,		eos_token_id=self.eos_token_id   ,		pad_token_id=self.pad_token_id   ,		rotary_dim=self.rotary_dim   ,		)

													return (config, input_ids, input_mask)



							def 			A__						(     self	)->      Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase        =	self.prepare_config_and_inputs()
													__UpperCamelCase        =	config_and_inputs
													__UpperCamelCase        =	{'input_ids': input_ids, 'attention_mask': attention_mask}
													return config, inputs_dict



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_	)->      Optional[int]:





													'''simple docstring'''






													__UpperCamelCase        =	20
													__UpperCamelCase        =	model_class_name(__UpperCAmelCase	)

													__UpperCamelCase        =	model.init_cache(input_ids.shape[0]   ,		__UpperCAmelCase	)
													__UpperCamelCase        =	jnp.ones((input_ids.shape[0], max_decoder_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 			A__						(     self   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_	)->      Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase        =	20
													__UpperCamelCase        =	model_class_name(__UpperCAmelCase	)

													__UpperCamelCase        =	jnp.concatenate(
													    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_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 				SCREAMING_SNAKE_CASE__			(							SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						unittest.TestCase			):

							"""simple docstring"""






							_snake_case   						=     (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							_snake_case   						=     (FlaxGPTJForCausalLM,) if is_flax_available() else ()



							def 			A__						(     self	)->      Dict:





													'''simple docstring'''






													__UpperCamelCase        =	FlaxGPTJModelTester(self	)



							def 			A__						(     self	)->      List[str]:





													'''simple docstring'''






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



							def 			A__						(     self	)->      List[str]:





													'''simple docstring'''






													for model_class_name in self.all_model_classes:
																			__UpperCamelCase        =	self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward_with_attn_mask(
																			    __UpperCAmelCase   ,		__UpperCAmelCase   ,		__UpperCAmelCase   ,		__UpperCAmelCase	)



							@tooslow
							def 			A__						(     self	)->      Dict:





													'''simple docstring'''






													__UpperCamelCase        =	GPTaTokenizer.from_pretrained('''gpt2'''   ,		pad_token='''<|endoftext|>'''   ,		padding_side='''left'''	)
													__UpperCamelCase        =	tokenizer(['''Hello this is a long string''', '''Hey''']   ,		return_tensors='''np'''   ,		padding=__UpperCAmelCase   ,		truncation=__UpperCAmelCase	)

													__UpperCamelCase        =	FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''	)
													__UpperCamelCase        =	False
													__UpperCamelCase        =	model.config.eos_token_id

													__UpperCamelCase        =	jax.jit(model.generate	)

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

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

													__UpperCamelCase        =	[
													    'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
													    'Hey, I\'m a little late to the party. I\'m going to',
													]

													self.assertListEqual(__UpperCAmelCase   ,		__UpperCAmelCase	)



							@is_pt_flax_cross_test
							def 			A__						(     self	)->      int:





													'''simple docstring'''






													__UpperCamelCase        =	self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__	):
																									# prepare inputs
																									__UpperCamelCase        =	self._prepare_for_class(__UpperCAmelCase   ,		__UpperCAmelCase	)
																									__UpperCamelCase        =	{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									__UpperCamelCase        =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									__UpperCamelCase        =	getattr(__UpperCAmelCase   ,		__UpperCAmelCase	)

																									__UpperCamelCase        =	pt_inputs['input_ids'].shape
																									__UpperCamelCase        =	np.random.randint(0   ,		seq_length - 1   ,		size=(batch_size,)	)
																									for batch_idx, start_index in enumerate(__UpperCAmelCase	):
																															__UpperCamelCase        =	0
																															__UpperCamelCase        =	1
																															__UpperCamelCase        =	0
																															__UpperCamelCase        =	1
																									__UpperCamelCase        =	pt_model_class(__UpperCAmelCase	).eval()
																									__UpperCamelCase        =	model_class(__UpperCAmelCase   ,		dtype=jnp.floataa	)

																									__UpperCamelCase        =	convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,		__UpperCAmelCase	)
																									__UpperCamelCase        =	fx_state

																									with torch.no_grad():
																															__UpperCamelCase        =	pt_model(**__UpperCAmelCase	).to_tuple()

																									__UpperCamelCase        =	fx_model(**__UpperCAmelCase	).to_tuple()
																									self.assertEqual(len(__UpperCAmelCase	)   ,		len(__UpperCAmelCase	)   ,		'''Output lengths differ between Flax and PyTorch'''	)
																									for fx_output, pt_output in zip(__UpperCAmelCase   ,		__UpperCAmelCase	):
																															self.assert_almost_equals(fx_output[:, -1]   ,		pt_output[:, -1].numpy()   ,		4E-2	)

																									with tempfile.TemporaryDirectory() as tmpdirname:
																															pt_model.save_pretrained(__UpperCAmelCase	)
																															__UpperCamelCase        =	model_class.from_pretrained(__UpperCAmelCase   ,		from_pt=__UpperCAmelCase	)

																									__UpperCamelCase        =	fx_model_loaded(**__UpperCAmelCase	).to_tuple()
																									self.assertEqual(
																									    len(__UpperCAmelCase	)   ,		len(__UpperCAmelCase	)   ,		'''Output lengths differ between Flax and PyTorch'''	)
																									for fx_output_loaded, pt_output in zip(__UpperCAmelCase   ,		__UpperCAmelCase	):
																															self.assert_almost_equals(fx_output_loaded[:, -1]   ,		pt_output[:, -1].numpy()   ,		4E-2	)



							@is_pt_flax_cross_test
							def 			A__						(     self	)->      Optional[int]:





													'''simple docstring'''






													__UpperCamelCase        =	self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__	):
																									# prepare inputs
																									__UpperCamelCase        =	self._prepare_for_class(__UpperCAmelCase   ,		__UpperCAmelCase	)
																									__UpperCamelCase        =	{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									__UpperCamelCase        =	model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									__UpperCamelCase        =	getattr(__UpperCAmelCase   ,		__UpperCAmelCase	)

																									__UpperCamelCase        =	pt_model_class(__UpperCAmelCase	).eval()
																									__UpperCamelCase        =	model_class(__UpperCAmelCase   ,		dtype=jnp.floataa	)

																									__UpperCamelCase        =	load_flax_weights_in_pytorch_model(__UpperCAmelCase   ,		fx_model.params	)
																									__UpperCamelCase        =	pt_inputs['input_ids'].shape
																									__UpperCamelCase        =	np.random.randint(0   ,		seq_length - 1   ,		size=(batch_size,)	)
																									for batch_idx, start_index in enumerate(__UpperCAmelCase	):
																															__UpperCamelCase        =	0
																															__UpperCamelCase        =	1
																															__UpperCamelCase        =	0
																															__UpperCamelCase        =	1

																									# make sure weights are tied in PyTorch
																									pt_model.tie_weights()

																									with torch.no_grad():
																															__UpperCamelCase        =	pt_model(**__UpperCAmelCase	).to_tuple()

																									__UpperCamelCase        =	fx_model(**__UpperCAmelCase	).to_tuple()
																									self.assertEqual(len(__UpperCAmelCase	)   ,		len(__UpperCAmelCase	)   ,		'''Output lengths differ between Flax and PyTorch'''	)
																									for fx_output, pt_output in zip(__UpperCAmelCase   ,		__UpperCAmelCase	):
																															self.assert_almost_equals(fx_output[:, -1]   ,		pt_output[:, -1].numpy()   ,		4E-2	)

																									with tempfile.TemporaryDirectory() as tmpdirname:
																															fx_model.save_pretrained(__UpperCAmelCase	)
																															__UpperCamelCase        =	pt_model_class.from_pretrained(__UpperCAmelCase   ,		from_flax=__UpperCAmelCase	)

																									with torch.no_grad():
																															__UpperCamelCase        =	pt_model_loaded(**__UpperCAmelCase	).to_tuple()

																									self.assertEqual(
																									    len(__UpperCAmelCase	)   ,		len(__UpperCAmelCase	)   ,		'''Output lengths differ between Flax and PyTorch'''	)
																									for fx_output, pt_output in zip(__UpperCAmelCase   ,		__UpperCAmelCase	):
																															self.assert_almost_equals(fx_output[:, -1]   ,		pt_output[:, -1].numpy()   ,		4E-2	)



							@tooslow
							def 			A__						(     self	)->      Union[str, Any]:





													'''simple docstring'''






													for model_class_name in self.all_model_classes:
																			__UpperCamelCase        =	model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''	)
																			__UpperCamelCase        =	model(np.ones((1, 1)	)	)
																			self.assertIsNotNone(__UpperCAmelCase	)











"""simple docstring"""





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:str											=							BertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
  print(F"Building PyTorch model from configuration: {config}"			)
  lowerCAmelCase__			:int											=							BertForPreTraining(_SCREAMING_SNAKE_CASE			)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  # Save pytorch-model
  print(F"Save PyTorch model to {pytorch_dump_path}"			)
  torch.save(model.state_dict()     ,   _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  __A											=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--bert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __A											=		parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
