



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        UpperCAmelCase_  :

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A=14				,							_A=7				,							_A=True				,							_A=True				,							_A=False				,							_A=True				,							_A=99				,							_A=32				,							_A=4				,							_A=4				,							_A=4				,							_A=37				,							_A="gelu"				,							_A=0.1				,							_A=0.1				,							_A=512				,							_A=0.0_2				,							):







													'''simple docstring'''

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






						def 	_A     (   self		):







													'''simple docstring'''

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

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

													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self		):







													'''simple docstring'''

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






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







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 20
													__SCREAMING_SNAKE_CASE									= model_class_name(_A		)

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

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

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

													__SCREAMING_SNAKE_CASE									= model(_A		)

													__SCREAMING_SNAKE_CASE									= 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				,							_A				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 20
													__SCREAMING_SNAKE_CASE									= model_class_name(_A		)

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

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

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

													__SCREAMING_SNAKE_CASE									= model(_A				,							attention_mask=_A		)

													__SCREAMING_SNAKE_CASE									= 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        UpperCAmelCase_  (       UpperCamelCase_   ,   UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Dict      =  (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
						UpperCamelCase__      :			Dict      =  (FlaxGPTJForCausalLM,) if is_flax_available() else ()






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= FlaxGPTJModelTester(self		)






						def 	_A     (   self		):







													'''simple docstring'''

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






						def 	_A     (   self		):







													'''simple docstring'''

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






						@tooslow
						def 	_A     (   self		):







													'''simple docstring'''

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

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

													__SCREAMING_SNAKE_CASE									= jax.jit(model.generate		)

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

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

													__SCREAMING_SNAKE_CASE									= [
													    '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 	_A     (   self		):







													'''simple docstring'''

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

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

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

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

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

																											__SCREAMING_SNAKE_CASE									= 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		)
																																		__SCREAMING_SNAKE_CASE									= model_class.from_pretrained(_A				,							from_pt=_A		)

																											__SCREAMING_SNAKE_CASE									= 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 	_A     (   self		):







													'''simple docstring'''

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

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

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

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

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

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

																											__SCREAMING_SNAKE_CASE									= 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		)
																																		__SCREAMING_SNAKE_CASE									= pt_model_class.from_pretrained(_A				,							from_flax=_A		)

																											with torch.no_grad():
																																		__SCREAMING_SNAKE_CASE									= 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 	_A     (   self		):







													'''simple docstring'''

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











import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  LayoutLMTokenizer
						UpperCamelCase__      :			Any      =  LayoutLMTokenizerFast
						UpperCamelCase__      :			Optional[int]      =  True
						UpperCamelCase__      :			int      =  True






						def 	_A     (   self		):







													'''simple docstring'''

													super().setUp()

													__SCREAMING_SNAKE_CASE									= [
													    '[UNK]',
													    '[CLS]',
													    '[SEP]',
													    'want',
													    '##want',
													    '##ed',
													    'wa',
													    'un',
													    'runn',
													    '##ing',
													    ',',
													    'low',
													    'lowest',
													]
													__SCREAMING_SNAKE_CASE									= os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['vocab_file']		)
													with open(self.vocab_file				,							'w'				,							encoding='utf-8'		) as vocab_writer:
																				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)






						def 	_A     (   self				,							**_A		):







													'''simple docstring'''

													return LayoutLMTokenizer.from_pretrained(self.tmpdirname				,							**_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'UNwant\u00E9d,running'
													__SCREAMING_SNAKE_CASE									= 'unwanted, running'
													return input_text, output_text






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.tokenizer_class(self.vocab_file		)

													__SCREAMING_SNAKE_CASE									= tokenizer.tokenize('UNwant\u00E9d,running'		)
													self.assertListEqual(_A				,							['un', '##want', '##ed', ',', 'runn', '##ing']		)
													self.assertListEqual(tokenizer.convert_tokens_to_ids(_A		)				,							[7, 4, 5, 10, 8, 9]		)






						def 	_A     (   self		):







													'''simple docstring'''

													pass






