


"""simple docstring"""






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      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : str ,       UpperCamelCase    : Optional[Any] ,       UpperCamelCase    : int=14 ,       UpperCamelCase    : List[Any]=7 ,       UpperCamelCase    : Tuple=True ,       UpperCamelCase    : List[Any]=True ,       UpperCamelCase    : Any=False ,       UpperCamelCase    : str=True ,       UpperCamelCase    : int=99 ,       UpperCamelCase    : Union[str, Any]=32 ,       UpperCamelCase    : int=4 ,       UpperCamelCase    : List[Any]=4 ,       UpperCamelCase    : Union[str, Any]=4 ,       UpperCamelCase    : Union[str, Any]=37 ,       UpperCamelCase    : Dict="gelu" ,       UpperCamelCase    : Any=0.1 ,       UpperCamelCase    : List[str]=0.1 ,       UpperCamelCase    : Union[str, Any]=512 ,       UpperCamelCase    : Union[str, Any]=0.02 ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[int]      							=					parent
												__UpperCAmelCase   :						Optional[int]      							=					batch_size
												__UpperCAmelCase   :						str      							=					seq_length
												__UpperCAmelCase   :						Union[str, Any]      							=					is_training
												__UpperCAmelCase   :						Tuple      							=					use_input_mask
												__UpperCAmelCase   :						List[str]      							=					use_token_type_ids
												__UpperCAmelCase   :						Tuple      							=					use_labels
												__UpperCAmelCase   :						int      							=					vocab_size
												__UpperCAmelCase   :						List[str]      							=					hidden_size
												__UpperCAmelCase   :						Any      							=					rotary_dim
												__UpperCAmelCase   :						int      							=					num_hidden_layers
												__UpperCAmelCase   :						Union[str, Any]      							=					num_attention_heads
												__UpperCAmelCase   :						Optional[int]      							=					intermediate_size
												__UpperCAmelCase   :						List[Any]      							=					hidden_act
												__UpperCAmelCase   :						List[str]      							=					hidden_dropout_prob
												__UpperCAmelCase   :						Any      							=					attention_probs_dropout_prob
												__UpperCAmelCase   :						List[str]      							=					max_position_embeddings
												__UpperCAmelCase   :						List[Any]      							=					initializer_range
												__UpperCAmelCase   :						str      							=					None
												__UpperCAmelCase   :						Optional[int]      							=					vocab_size - 1
												__UpperCAmelCase   :						List[Any]      							=					vocab_size - 1
												__UpperCAmelCase   :						Dict      							=					vocab_size - 1






					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size  )

												__UpperCAmelCase   :						Optional[Any]      							=					None
												if self.use_input_mask:
																			__UpperCAmelCase   :						str      							=					random_attention_mask([self.batch_size, self.seq_length]  )

												__UpperCAmelCase   :						str      							=					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 lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					self.prepare_config_and_inputs()
												__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						Union[str, Any]      							=					config_and_inputs
												__UpperCAmelCase   :						List[Any]      							=					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
												return config, inputs_dict






					def lowerCamelCase__							(       self    : Tuple ,       UpperCamelCase    : Optional[Any] ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : List[Any] ,       UpperCamelCase    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					20
												__UpperCAmelCase   :						Any      							=					model_class_name(UpperCamelCase  )

												__UpperCAmelCase   :						List[Any]      							=					model.init_cache(input_ids.shape[0] ,       UpperCamelCase  )
												__UpperCAmelCase   :						Dict      							=					jnp.ones((input_ids.shape[0], max_decoder_length) ,       dtype="""i4"""  )

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

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

												__UpperCAmelCase   :						Optional[int]      							=					model(UpperCamelCase  )

												__UpperCAmelCase   :						int      							=					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 lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : List[Any] ,       UpperCamelCase    : str ,       UpperCamelCase    : Optional[Any] ,       UpperCamelCase    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					20
												__UpperCAmelCase   :						int      							=					model_class_name(UpperCamelCase  )

												__UpperCAmelCase   :						Optional[Any]      							=					jnp.concatenate(
												    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )] ,       axis=-1 ,       )

												__UpperCAmelCase   :						Union[str, Any]      							=					model.init_cache(input_ids.shape[0] ,       UpperCamelCase  )
												__UpperCAmelCase   :						Dict      							=					jnp.broadcast_to(
												    jnp.arange(input_ids.shape[-1] - 1  )[None, :] ,       (input_ids.shape[0], input_ids.shape[-1] - 1)  )

												__UpperCAmelCase   :						int      							=					model(
												    input_ids[:, :-1] ,       attention_mask=UpperCamelCase ,       past_key_values=UpperCamelCase ,       position_ids=UpperCamelCase ,       )
												__UpperCAmelCase   :						Optional[Any]      							=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype="""i4"""  )
												__UpperCAmelCase   :						int      							=					model(
												    input_ids[:, -1:] ,       past_key_values=outputs_cache.past_key_values ,       attention_mask=UpperCamelCase ,       position_ids=UpperCamelCase ,       )

												__UpperCAmelCase   :						Any      							=					model(UpperCamelCase ,       attention_mask=UpperCamelCase  )

												__UpperCAmelCase   :						Optional[Any]      							=					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__   (     A     ,    A     ,    unittest.TestCase				):







					"""simple docstring"""


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






					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					FlaxGPTJModelTester(self  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												for model_class_name in self.all_model_classes:
																			__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						Optional[Any]      							=					self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward(UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase  )






					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






												for model_class_name in self.all_model_classes:
																			__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						List[str]      							=					self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward_with_attn_mask(
																			    UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase  )






					@tooslow
					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






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

												__UpperCAmelCase   :						Optional[int]      							=					FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""  )
												__UpperCAmelCase   :						Optional[Any]      							=					False
												__UpperCAmelCase   :						Optional[int]      							=					model.config.eos_token_id

												__UpperCAmelCase   :						List[str]      							=					jax.jit(model.generate  )

												__UpperCAmelCase   :						str      							=					jit_generate(
												    inputs["""input_ids"""] ,       attention_mask=inputs["""attention_mask"""] ,       pad_token_id=tokenizer.pad_token_id  ).sequences

												__UpperCAmelCase   :						Tuple      							=					tokenizer.batch_decode(UpperCamelCase ,       skip_special_tokens=UpperCamelCase  )

												__UpperCAmelCase   :						Tuple      							=					[
												    """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 lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase							,__UpperCAmelCase   :						Any      							=					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   :						Any      							=					self._prepare_for_class(UpperCamelCase ,       UpperCamelCase  )
																										__UpperCAmelCase   :						Any      							=					{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

																										__UpperCAmelCase							,__UpperCAmelCase   :						Any      							=					pt_inputs["""input_ids"""].shape
																										__UpperCAmelCase   :						str      							=					np.random.randint(0 ,       seq_length - 1 ,       size=(batch_size,)  )
																										for batch_idx, start_index in enumerate(UpperCamelCase  ):
																																	__UpperCAmelCase   :						str      							=					0
																																	__UpperCAmelCase   :						Optional[Any]      							=					1
																																	__UpperCAmelCase   :						Optional[int]      							=					0
																																	__UpperCAmelCase   :						Tuple      							=					1
																										__UpperCAmelCase   :						str      							=					pt_model_class(UpperCamelCase  ).eval()
																										__UpperCAmelCase   :						Optional[Any]      							=					model_class(UpperCamelCase ,       dtype=jnp.floataa  )

																										__UpperCAmelCase   :						Dict      							=					convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,       UpperCamelCase  )
																										__UpperCAmelCase   :						str      							=					fx_state

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

																										__UpperCAmelCase   :						List[str]      							=					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   :						List[Any]      							=					model_class.from_pretrained(UpperCamelCase ,       from_pt=UpperCamelCase  )

																										__UpperCAmelCase   :						Union[str, Any]      							=					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 lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase							,__UpperCAmelCase   :						Tuple      							=					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   :						str      							=					self._prepare_for_class(UpperCamelCase ,       UpperCamelCase  )
																										__UpperCAmelCase   :						List[str]      							=					{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

																										__UpperCAmelCase   :						int      							=					pt_model_class(UpperCamelCase  ).eval()
																										__UpperCAmelCase   :						Optional[int]      							=					model_class(UpperCamelCase ,       dtype=jnp.floataa  )

																										__UpperCAmelCase   :						Union[str, Any]      							=					load_flax_weights_in_pytorch_model(UpperCamelCase ,       fx_model.params  )
																										__UpperCAmelCase							,__UpperCAmelCase   :						Any      							=					pt_inputs["""input_ids"""].shape
																										__UpperCAmelCase   :						Dict      							=					np.random.randint(0 ,       seq_length - 1 ,       size=(batch_size,)  )
																										for batch_idx, start_index in enumerate(UpperCamelCase  ):
																																	__UpperCAmelCase   :						Tuple      							=					0
																																	__UpperCAmelCase   :						Any      							=					1
																																	__UpperCAmelCase   :						List[str]      							=					0
																																	__UpperCAmelCase   :						int      							=					1

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

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

																										__UpperCAmelCase   :						List[str]      							=					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   :						Dict      							=					pt_model_class.from_pretrained(UpperCamelCase ,       from_flax=UpperCamelCase  )

																										with torch.no_grad():
																																	__UpperCAmelCase   :						Any      							=					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 lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												for model_class_name in self.all_model_classes:
																			__UpperCAmelCase   :						Optional[Any]      							=					model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""  )
																			__UpperCAmelCase   :						str      							=					model(np.ones((1, 1)  )  )
																			self.assertIsNotNone(UpperCamelCase  )




"""simple docstring"""






import re
def      lowerCamelCase       (     _UpperCamelCase		:      str							)		->       str:

							'''simple docstring'''



							if len(re.findall("""[ATCG]"""       ,			_UpperCamelCase							)							) != len(_UpperCamelCase							):
														raise ValueError("""Invalid Strand"""							)

							return dna.translate(dna.maketrans("""ATCG"""       ,			"""TAGC"""							)							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
