




from pathlib import Path

import fire
from tqdm import tqdm







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict="ro"				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]="en"				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]="wmt16"				,				SCREAMING_SNAKE_CASE_							:    List[str]=None					):
					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError("run pip install datasets"					)
					__lowerCAmelCase           =	F"""{src_lang}-{tgt_lang}"""
					print(F"""Converting {dataset}-{pair}"""					)
					__lowerCAmelCase           =	datasets.load_dataset(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					if save_dir is None:
										__lowerCAmelCase           =	F"""{dataset}-{pair}"""
					__lowerCAmelCase           =	Path(SCREAMING_SNAKE_CASE_					)
					save_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)

					for split in ds.keys():
										print(F"""Splitting {split} with {ds[split].num_rows} records"""					)

										# to save to val.source, val.target like summary datasets
										__lowerCAmelCase           =	"val" if split == "validation" else split
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.source"""					)
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.target"""					)
										__lowerCAmelCase           =	src_path.open("w+"					)
										__lowerCAmelCase           =	tgt_path.open("w+"					)

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]					):
															__lowerCAmelCase           =	x["translation"]
															src_fp.write(ex[src_lang] + "\n"					)
															tgt_fp.write(ex[tgt_lang] + "\n"					)

					print(F"""Saved {dataset} dataset to {save_dir}"""					)


if __name__ == "__main__":
					fire.Fire(download_wmt_dataset)










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       a__   :






						def __init__(       self		,      _A		,      _A=1_4		,      _A=7		,      _A=True		,      _A=True		,      _A=False		,      _A=True		,      _A=9_9		,      _A=3_2		,      _A=4		,      _A=4		,      _A=4		,      _A=3_7		,      _A="gelu"		,      _A=0.1		,      _A=0.1		,      _A=5_1_2		,      _A=0.02		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	parent
											__lowerCAmelCase           =	batch_size
											__lowerCAmelCase           =	seq_length
											__lowerCAmelCase           =	is_training
											__lowerCAmelCase           =	use_input_mask
											__lowerCAmelCase           =	use_token_type_ids
											__lowerCAmelCase           =	use_labels
											__lowerCAmelCase           =	vocab_size
											__lowerCAmelCase           =	hidden_size
											__lowerCAmelCase           =	rotary_dim
											__lowerCAmelCase           =	num_hidden_layers
											__lowerCAmelCase           =	num_attention_heads
											__lowerCAmelCase           =	intermediate_size
											__lowerCAmelCase           =	hidden_act
											__lowerCAmelCase           =	hidden_dropout_prob
											__lowerCAmelCase           =	attention_probs_dropout_prob
											__lowerCAmelCase           =	max_position_embeddings
											__lowerCAmelCase           =	initializer_range
											__lowerCAmelCase           =	None
											__lowerCAmelCase           =	vocab_size - 1
											__lowerCAmelCase           =	vocab_size - 1
											__lowerCAmelCase           =	vocab_size - 1






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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

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

											__lowerCAmelCase           =	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=_A		,      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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	2_0
											__lowerCAmelCase           =	model_class_name(_A      )

											__lowerCAmelCase           =	model.init_cache(input_ids.shape[0]		,      _A      )
											__lowerCAmelCase           =	jnp.ones((input_ids.shape[0], max_decoder_length)		,      dtype="i4"      )

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

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

											__lowerCAmelCase           =	model(_A      )

											__lowerCAmelCase           =	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     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	2_0
											__lowerCAmelCase           =	model_class_name(_A      )

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

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

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

											__lowerCAmelCase           =	model(_A		,      attention_mask=_A      )

											__lowerCAmelCase           =	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       a__   ( snake_case__			,    snake_case__			,    unittest.TestCase				):
						_a							:   str =   (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
						_a							:   Any =   (FlaxGPTJForCausalLM,) if is_flax_available() else ()






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	FlaxGPTJModelTester(self      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_class_name in self.all_model_classes:
																__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
																self.model_tester.check_use_cache_forward(_A		,      _A		,      _A		,      _A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											for model_class_name in self.all_model_classes:
																__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	self.model_tester.prepare_config_and_inputs()
																self.model_tester.check_use_cache_forward_with_attn_mask(
																    _A		,      _A		,      _A		,      _A      )






						@tooslow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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

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

											__lowerCAmelCase           =	jax.jit(model.generate      )

											__lowerCAmelCase           =	jit_generate(
											    inputs["input_ids"]		,      attention_mask=inputs["attention_mask"]		,      pad_token_id=tokenizer.pad_token_id      ).sequences

											__lowerCAmelCase           =	tokenizer.batch_decode(_A		,      skip_special_tokens=_A      )

											__lowerCAmelCase           =	[
											    "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(_A		,      _A      )






						@is_pt_flax_cross_test
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase       ,							__lowerCAmelCase           =	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
																					__lowerCAmelCase           =	self._prepare_for_class(_A		,      _A      )
																					__lowerCAmelCase           =	{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

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

																					__lowerCAmelCase           =	convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,      _A      )
																					__lowerCAmelCase           =	fx_state

																					with torch.no_grad():
																										__lowerCAmelCase           =	pt_model(**_A      ).to_tuple()

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

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

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






						@is_pt_flax_cross_test
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase       ,							__lowerCAmelCase           =	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
																					__lowerCAmelCase           =	self._prepare_for_class(_A		,      _A      )
																					__lowerCAmelCase           =	{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

																					__lowerCAmelCase           =	pt_model_class(_A      ).eval()
																					__lowerCAmelCase           =	model_class(_A		,      dtype=jnp.floataa      )

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

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

																					with torch.no_grad():
																										__lowerCAmelCase           =	pt_model(**_A      ).to_tuple()

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

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

																					with torch.no_grad():
																										__lowerCAmelCase           =	pt_model_loaded(**_A      ).to_tuple()

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






						@tooslow
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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




