




'''simple docstring'''






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
				import torch



class    A						:




				'''simple docstring'''





				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase=1_4					,    _UpperCAmelCase=7					,    _UpperCAmelCase=True					,    _UpperCAmelCase=True					,    _UpperCAmelCase=False					,    _UpperCAmelCase=True					,    _UpperCAmelCase=9_9					,    _UpperCAmelCase=3_2					,    _UpperCAmelCase=5					,    _UpperCAmelCase=4					,    _UpperCAmelCase=3_7					,    _UpperCAmelCase="gelu"					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=5_1_2					,    _UpperCAmelCase=0.02					,    )      ->			Optional[Any]:
							__UpperCamelCase :     Union[str, Any]										=  parent
							__UpperCamelCase :     Any										=  batch_size
							__UpperCamelCase :     str										=  seq_length
							__UpperCamelCase :     str										=  is_training
							__UpperCamelCase :     Dict										=  use_input_mask
							__UpperCamelCase :     Dict										=  use_token_type_ids
							__UpperCamelCase :     str										=  use_labels
							__UpperCamelCase :     Optional[int]										=  vocab_size
							__UpperCamelCase :     Any										=  hidden_size
							__UpperCamelCase :     Tuple										=  num_hidden_layers
							__UpperCamelCase :     Dict										=  num_attention_heads
							__UpperCamelCase :     List[Any]										=  intermediate_size
							__UpperCamelCase :     Tuple										=  hidden_act
							__UpperCamelCase :     Any										=  hidden_dropout_prob
							__UpperCamelCase :     Any										=  attention_probs_dropout_prob
							__UpperCamelCase :     List[Any]										=  max_position_embeddings
							__UpperCamelCase :     int										=  initializer_range
							__UpperCamelCase :     int										=  None
							__UpperCamelCase :     Any										=  vocab_size - 1
							__UpperCamelCase :     Optional[int]										=  vocab_size - 1
							__UpperCamelCase :     List[Any]										=  vocab_size - 1
				def 			a_			(self     )      ->			List[str]:
							__UpperCamelCase :     Dict										=  ids_tensor([self.batch_size, self.seq_length]					,    self.vocab_size     )

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

							__UpperCamelCase :     Tuple										=  GPTaConfig(
							    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					,    )

							return (config, input_ids, input_mask)
				def 			a_			(self     )      ->			List[str]:
							__UpperCamelCase :     Any										=  self.prepare_config_and_inputs()
							__UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase :     Optional[int]										=  config_and_inputs
							__UpperCamelCase :     Tuple										=  {"input_ids": input_ids, "attention_mask": attention_mask}
							return config, inputs_dict
				def 			a_			(self     )      ->			List[str]:
							__UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase :     Dict										=  self.prepare_config_and_inputs()

							__UpperCamelCase :     int										=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
							__UpperCamelCase :     Union[str, Any]										=  ids_tensor([self.batch_size, self.seq_length]					,    vocab_size=2     )

							return (
							    config,
							    input_ids,
							    attention_mask,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			Tuple:
							__UpperCamelCase :     Tuple										=  2_0
							__UpperCamelCase :     int										=  model_class_name(_UpperCAmelCase     )

							__UpperCamelCase :     List[Any]										=  model.init_cache(input_ids.shape[0]					,    _UpperCAmelCase     )
							__UpperCamelCase :     str										=  jnp.ones((input_ids.shape[0], max_decoder_length)					,    dtype="i4"     )

							__UpperCamelCase :     Optional[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=_UpperCAmelCase					,    past_key_values=_UpperCAmelCase					,    position_ids=_UpperCAmelCase					,    )

							__UpperCamelCase :     Optional[int]										=  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 :     List[str]										=  model(_UpperCAmelCase     )

							__UpperCamelCase :     List[str]										=  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					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			List[str]:
							__UpperCamelCase :     Dict										=  2_0
							__UpperCamelCase :     int										=  model_class_name(_UpperCAmelCase     )

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

							__UpperCamelCase :     List[str]										=  model.init_cache(input_ids.shape[0]					,    _UpperCAmelCase     )
							__UpperCamelCase :     Any										=  jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1     )[None, :]					,    (input_ids.shape[0], input_ids.shape[-1] - 1)     )

							__UpperCamelCase :     List[Any]										=  model(
							    input_ids[:, :-1]					,    attention_mask=_UpperCAmelCase					,    past_key_values=_UpperCAmelCase					,    position_ids=_UpperCAmelCase					,    )
							__UpperCamelCase :     str										=  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=_UpperCAmelCase					,    position_ids=_UpperCAmelCase					,    )

							__UpperCamelCase :     Optional[Any]										=  model(_UpperCAmelCase					,    attention_mask=_UpperCAmelCase     )

							__UpperCamelCase :     List[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    A						(					SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				unittest.TestCase						):




				'''simple docstring'''





				A       =							(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
				A       =							(FlaxGPTaLMHeadModel,) if is_flax_available() else ()
				def 			a_			(self     )      ->			Union[str, Any]:
							__UpperCamelCase :     Optional[int]										=  FlaxGPTaModelTester(self     )
				def 			a_			(self     )      ->			List[str]:
							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(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )
				def 			a_			(self     )      ->			Dict:
							for model_class_name in self.all_model_classes:
										__UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase :     List[Any]										=  self.model_tester.prepare_config_and_inputs()
										self.model_tester.check_use_cache_forward_with_attn_mask(
										    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )
				@slow
				def 			a_			(self     )      ->			str:
							__UpperCamelCase :     Tuple										=  GPTaTokenizer.from_pretrained("gpt2"					,    pad_token="</s>"					,    padding_side="left"     )
							__UpperCamelCase :     Optional[Any]										=  tokenizer(["Hello this is a long string", "Hey"]					,    return_tensors="np"					,    padding=_UpperCAmelCase					,    truncation=_UpperCAmelCase     )

							__UpperCamelCase :     Any										=  FlaxGPTaLMHeadModel.from_pretrained("gpt2"     )
							__UpperCamelCase :     str										=  False
							__UpperCamelCase :     Any										=  model.config.eos_token_id

							__UpperCamelCase :     Optional[Any]										=  jax.jit(model.generate     )

							__UpperCamelCase :     Optional[int]										=  jit_generate(inputs["input_ids"]					,    attention_mask=inputs["attention_mask"]     ).sequences

							__UpperCamelCase :     Any										=  tokenizer.batch_decode(_UpperCAmelCase					,    skip_special_tokens=_UpperCAmelCase     )

							__UpperCamelCase :     int										=  [
							    "Hello this is a long string of words. I'm going to start with the first one.\n",
							    "Hey, I'm not sure if I'm going to be able to do",
							]

							self.assertListEqual(_UpperCAmelCase					,    _UpperCAmelCase     )
				@is_pt_flax_cross_test
				def 			a_			(self     )      ->			Dict:
							__UpperCamelCase						,   __UpperCamelCase :     List[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 :     Dict										=  self._prepare_for_class(_UpperCAmelCase					,    _UpperCAmelCase     )
													__UpperCamelCase :     str										=  {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 :     Tuple										=  getattr(_UpperCAmelCase					,    _UpperCAmelCase     )

													__UpperCamelCase						,   __UpperCamelCase :     str										=  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 :     Optional[int]										=  0
																__UpperCamelCase :     List[str]										=  1
																__UpperCamelCase :     Tuple										=  0
																__UpperCamelCase :     Any										=  1
													__UpperCamelCase :     str										=  pt_model_class(_UpperCAmelCase     ).eval()
													__UpperCamelCase :     Optional[Any]										=  model_class(_UpperCAmelCase					,    dtype=jnp.floataa     )

													__UpperCamelCase :     Tuple										=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,    _UpperCAmelCase     )
													__UpperCamelCase :     Tuple										=  fx_state

													with torch.no_grad():
																__UpperCamelCase :     Optional[int]										=  pt_model(**_UpperCAmelCase     ).to_tuple()

													__UpperCamelCase :     Optional[Any]										=  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 :     Tuple										=  model_class.from_pretrained(_UpperCAmelCase					,    from_pt=_UpperCAmelCase     )

													__UpperCamelCase :     List[str]										=  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     )      ->			Tuple:
							__UpperCamelCase						,   __UpperCamelCase :     List[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 :     Optional[Any]										=  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 :     Dict										=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
													__UpperCamelCase :     List[str]										=  getattr(_UpperCAmelCase					,    _UpperCAmelCase     )

													__UpperCamelCase :     Optional[Any]										=  pt_model_class(_UpperCAmelCase     ).eval()
													__UpperCamelCase :     Union[str, Any]										=  model_class(_UpperCAmelCase					,    dtype=jnp.floataa     )

													__UpperCamelCase :     str										=  load_flax_weights_in_pytorch_model(_UpperCAmelCase					,    fx_model.params     )
													__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 :     Optional[Any]										=  0
																__UpperCamelCase :     Tuple										=  1
																__UpperCamelCase :     Dict										=  0
																__UpperCamelCase :     Union[str, Any]										=  1

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

													with torch.no_grad():
																__UpperCamelCase :     Optional[Any]										=  pt_model(**_UpperCAmelCase     ).to_tuple()

													__UpperCamelCase :     Optional[Any]										=  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 :     Optional[int]										=  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     )





				@slow
				def 			a_			(self     )      ->			Optional[Any]:
							for model_class_name in self.all_model_classes:
										__UpperCamelCase :     int										=  model_class_name.from_pretrained("gpt2"					,    from_pt=_UpperCAmelCase     )
										__UpperCamelCase :     Optional[Any]										=  model(np.ones((1, 1)     )     )
										self.assertIsNotNone(_UpperCAmelCase     )









'''simple docstring'''






def      __lowerCAmelCase   (       snake_case__		):
			__UpperCamelCase :     int										=  word.upper()
			__UpperCamelCase :     List[str]										=  0
			__UpperCamelCase :     int										=  {}
			__UpperCamelCase :     Tuple										=  []

			for letter in word:
						if letter not in letter_nums:
									__UpperCamelCase :     Tuple										=  str(snake_case__		)
									next_num += 1
						word_pattern.append(letter_nums[letter]		)
			return ".".join(snake_case__		)


if __name__ == "__main__":
				import pprint
				import time

				_lowerCAmelCase       =   time.time()
				with open('''dictionary.txt''') as in_file:
								_lowerCAmelCase       =   in_file.read().splitlines()

				_lowerCAmelCase       =   {}
				for word in word_list:
								_lowerCAmelCase       =   get_word_pattern(word)
								if pattern in all_patterns:
												all_patterns[pattern].append(word)
								else:
												_lowerCAmelCase       =   [word]

				with open('''word_patterns.txt''', '''w''') as out_file:
								out_file.write(pprint.pformat(all_patterns))

				_lowerCAmelCase       =   round(time.time() - start_time, 2)
				print(f'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
				# Done!  9,581 word patterns found in 0.58 seconds.



