



'''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 				lowercase__      :





	def __init__(					self   :							Tuple					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							List[str]=14					,lowerCamelCase__   :							Union[str, Any]=7					,lowerCamelCase__   :							Dict=True					,lowerCamelCase__   :							Dict=True					,lowerCamelCase__   :							Any=False					,lowerCamelCase__   :							Optional[int]=True					,lowerCamelCase__   :							str=99					,lowerCamelCase__   :							Optional[Any]=32					,lowerCamelCase__   :							Optional[int]=4					,lowerCamelCase__   :							List[str]=4					,lowerCamelCase__   :							Union[str, Any]=4					,lowerCamelCase__   :							str=37					,lowerCamelCase__   :							Any="gelu"					,lowerCamelCase__   :							int=0.1					,lowerCamelCase__   :							int=0.1					,lowerCamelCase__   :							Union[str, Any]=512					,lowerCamelCase__   :							Any=0.0_2					,):







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





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size					)

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

							_UpperCamelCase   :						List[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=lowerCamelCase__					,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       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  self.prepare_config_and_inputs()
							_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						str												=  config_and_inputs
							_UpperCamelCase   :						List[Any]												=  {'input_ids': input_ids, 'attention_mask': attention_mask}
							return config, inputs_dict





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							Optional[int]					,lowerCamelCase__   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  20
							_UpperCamelCase   :						List[Any]												=  model_class_name(lowerCamelCase__					)

							_UpperCamelCase   :						Dict												=  model.init_cache(input_ids.shape[0]					,lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  jnp.ones((input_ids.shape[0], max_decoder_length)					,dtype='i4'					)

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

							_UpperCamelCase   :						int												=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,dtype='i4'					)
							_UpperCamelCase   :						List[str]												=  model(
							    input_ids[:, -1:]					,attention_mask=lowerCamelCase__					,past_key_values=outputs_cache.past_key_values					,position_ids=lowerCamelCase__					,)

							_UpperCamelCase   :						List[str]												=  model(lowerCamelCase__					)

							_UpperCamelCase   :						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}'					)





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							str					,lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							List[Any]					,lowerCamelCase__   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  20
							_UpperCamelCase   :						Tuple												=  model_class_name(lowerCamelCase__					)

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

							_UpperCamelCase   :						int												=  model.init_cache(input_ids.shape[0]					,lowerCamelCase__					)
							_UpperCamelCase   :						Optional[int]												=  jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1					)[None, :]					,(input_ids.shape[0], input_ids.shape[-1] - 1)					)

							_UpperCamelCase   :						Union[str, Any]												=  model(
							    input_ids[:, :-1]					,attention_mask=lowerCamelCase__					,past_key_values=lowerCamelCase__					,position_ids=lowerCamelCase__					,)
							_UpperCamelCase   :						Optional[int]												=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,dtype='i4'					)
							_UpperCamelCase   :						Optional[int]												=  model(
							    input_ids[:, -1:]					,past_key_values=outputs_cache.past_key_values					,attention_mask=lowerCamelCase__					,position_ids=lowerCamelCase__					,)

							_UpperCamelCase   :						Tuple												=  model(lowerCamelCase__					,attention_mask=lowerCamelCase__					)

							_UpperCamelCase   :						Union[str, 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 				lowercase__      (				lowercase		,   lowercase		,   unittest.TestCase  ):
	lowercase__    							=     (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
	lowercase__    							=     (FlaxGPTJForCausalLM,) if is_flax_available() else ()





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  FlaxGPTJModelTester(self					)





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







							'''simple docstring'''
							for model_class_name in self.all_model_classes:
													_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						Dict												=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							for model_class_name in self.all_model_classes:
													_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						Union[str, Any]												=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward_with_attn_mask(
													    lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)





	@tooslow
	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  GPTaTokenizer.from_pretrained('gpt2'					,pad_token='<|endoftext|>'					,padding_side='left'					)
							_UpperCamelCase   :						Union[str, Any]												=  tokenizer(['Hello this is a long string', 'Hey']					,return_tensors='np'					,padding=lowerCamelCase__					,truncation=lowerCamelCase__					)

							_UpperCamelCase   :						Union[str, Any]												=  FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'					)
							_UpperCamelCase   :						str												=  False
							_UpperCamelCase   :						Optional[Any]												=  model.config.eos_token_id

							_UpperCamelCase   :						int												=  jax.jit(model.generate					)

							_UpperCamelCase   :						Union[str, Any]												=  jit_generate(
							    inputs['input_ids']					,attention_mask=inputs['attention_mask']					,pad_token_id=tokenizer.pad_token_id					).sequences

							_UpperCamelCase   :						str												=  tokenizer.batch_decode(lowerCamelCase__					,skip_special_tokens=lowerCamelCase__					)

							_UpperCamelCase   :						Optional[Any]												=  [
							    '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(lowerCamelCase__					,lowerCamelCase__					)





	@is_pt_flax_cross_test
	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''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   :						Dict												=  self._prepare_for_class(lowerCamelCase__					,lowerCamelCase__					)
																			_UpperCamelCase   :						Dict												=  {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

																			_UpperCamelCase							,     _UpperCamelCase   :						str												=  pt_inputs['input_ids'].shape
																			_UpperCamelCase   :						int												=  np.random.randint(0					,seq_length - 1					,size=(batch_size,)					)
																			for batch_idx, start_index in enumerate(lowerCamelCase__					):
																									_UpperCamelCase   :						Tuple												=  0
																									_UpperCamelCase   :						List[str]												=  1
																									_UpperCamelCase   :						Dict												=  0
																									_UpperCamelCase   :						Dict												=  1
																			_UpperCamelCase   :						List[Any]												=  pt_model_class(lowerCamelCase__					).eval()
																			_UpperCamelCase   :						str												=  model_class(lowerCamelCase__					,dtype=jnp.floataa					)

																			_UpperCamelCase   :						Optional[int]												=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,lowerCamelCase__					)
																			_UpperCamelCase   :						Any												=  fx_state

																			with torch.no_grad():
																									_UpperCamelCase   :						Optional[Any]												=  pt_model(**lowerCamelCase__					).to_tuple()

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

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									pt_model.save_pretrained(lowerCamelCase__					)
																									_UpperCamelCase   :						Dict												=  model_class.from_pretrained(lowerCamelCase__					,from_pt=lowerCamelCase__					)

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





	@is_pt_flax_cross_test
	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						str												=  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   :						Optional[int]												=  self._prepare_for_class(lowerCamelCase__					,lowerCamelCase__					)
																			_UpperCamelCase   :						Tuple												=  {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   :						str												=  getattr(lowerCamelCase__					,lowerCamelCase__					)

																			_UpperCamelCase   :						int												=  pt_model_class(lowerCamelCase__					).eval()
																			_UpperCamelCase   :						List[Any]												=  model_class(lowerCamelCase__					,dtype=jnp.floataa					)

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

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

																			with torch.no_grad():
																									_UpperCamelCase   :						Any												=  pt_model(**lowerCamelCase__					).to_tuple()

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

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									fx_model.save_pretrained(lowerCamelCase__					)
																									_UpperCamelCase   :						List[str]												=  pt_model_class.from_pretrained(lowerCamelCase__					,from_flax=lowerCamelCase__					)

																			with torch.no_grad():
																									_UpperCamelCase   :						str												=  pt_model_loaded(**lowerCamelCase__					).to_tuple()

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





	@tooslow
	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							for model_class_name in self.all_model_classes:
													_UpperCamelCase   :						Optional[int]												=  model_class_name.from_pretrained('EleutherAI/gpt-j-6B'					)
													_UpperCamelCase   :						Optional[int]												=  model(np.ones((1, 1)					)					)
													self.assertIsNotNone(lowerCamelCase__					)







'''simple docstring'''
from pathlib import Path

import fire






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						int												=  Path(UpperCAmelCase_				)
						_UpperCamelCase   :						str												=  Path(UpperCAmelCase_				)
						dest_dir.mkdir(exist_ok=UpperCAmelCase_				)
						for path in src_dir.iterdir():
												_UpperCamelCase   :						int												=  [x.rstrip() for x in list(path.open().readlines()				)][:n]
												_UpperCamelCase   :						Any												=  dest_dir.joinpath(path.name				)
												print(UpperCAmelCase_				)
												dest_path.open('w'				).write('\n'.join(UpperCAmelCase_				)				)


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


