
'''simple docstring'''

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
		import torch






class    A     :





	def __init__(					self			:       Any      ,							__magic_name__			:       Optional[int]      ,							__magic_name__			:       Optional[int]=14      ,							__magic_name__			:       int=7      ,							__magic_name__			:       str=True      ,							__magic_name__			:       Union[str, Any]=True      ,							__magic_name__			:       List[str]=False      ,							__magic_name__			:       Tuple=True      ,							__magic_name__			:       List[str]=99      ,							__magic_name__			:       List[Any]=32      ,							__magic_name__			:       Optional[Any]=4      ,							__magic_name__			:       Any=4      ,							__magic_name__			:       Optional[int]=[[["global", "local"], 2]]      ,							__magic_name__			:       Dict=37      ,							__magic_name__			:       List[str]="gelu"      ,							__magic_name__			:       List[Any]=0.1      ,							__magic_name__			:       List[str]=0.1      ,							__magic_name__			:       Tuple=512      ,							__magic_name__			:       int=7      ,							__magic_name__			:       Dict=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__								=							num_hidden_layers
				lowerCAmelCase__								=							num_attention_heads
				lowerCAmelCase__								=							attention_types
				lowerCAmelCase__								=							intermediate_size
				lowerCAmelCase__								=							hidden_act
				lowerCAmelCase__								=							hidden_dropout_prob
				lowerCAmelCase__								=							attention_probs_dropout_prob
				lowerCAmelCase__								=							max_position_embeddings
				lowerCAmelCase__								=							window_size
				lowerCAmelCase__								=							initializer_range
				lowerCAmelCase__								=							None
				lowerCAmelCase__								=							vocab_size - 1
				lowerCAmelCase__								=							vocab_size - 1
				lowerCAmelCase__								=							vocab_size - 1





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""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__								=							GPTNeoConfig(
				    vocab_size=self.vocab_size      ,							hidden_size=self.hidden_size      ,							num_layers=self.num_hidden_layers      ,							num_heads=self.num_attention_heads      ,							max_position_embeddings=self.max_position_embeddings      ,							use_cache=__magic_name__      ,							bos_token_id=self.bos_token_id      ,							eos_token_id=self.eos_token_id      ,							pad_token_id=self.pad_token_id      ,							window_size=self.window_size      ,							attention_types=self.attention_types      ,							)

				return (config, input_ids, input_mask)





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


				"""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			:       Tuple      ,							__magic_name__			:       Dict      ,							__magic_name__			:       List[Any]      ,							__magic_name__			:       int      ,							__magic_name__			:       Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__								=							20
				lowerCAmelCase__								=							model_class_name(__magic_name__							)

				lowerCAmelCase__								=							model.init_cache(input_ids.shape[0]      ,							__magic_name__							)
				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=__magic_name__      ,							past_key_values=__magic_name__      ,							position_ids=__magic_name__      ,							)

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

				lowerCAmelCase__								=							model(__magic_name__							)

				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			:       List[Any]      ,							__magic_name__			:       int      ,							__magic_name__			:       str      ,							__magic_name__			:       Any      ,							__magic_name__			:       List[str]							):


				"""simple docstring"""

				lowerCAmelCase__								=							20
				lowerCAmelCase__								=							model_class_name(__magic_name__							)

				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]      ,							__magic_name__							)
				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=__magic_name__      ,							past_key_values=__magic_name__      ,							position_ids=__magic_name__      ,							)
				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=__magic_name__      ,							position_ids=__magic_name__      ,							)

				lowerCAmelCase__								=							model(__magic_name__      ,							attention_mask=__magic_name__							)

				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     (	SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      unittest.TestCase    ):
	snake_case__      :Dict        =     (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
	snake_case__      :List[Any]        =     (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__								=							FlaxGPTNeoModelTester(self							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]							):


				"""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(__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""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(
							    __magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__							)





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[int]							):


				"""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=__magic_name__      ,							truncation=__magic_name__							)

				lowerCAmelCase__								=							FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"							)
				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(__magic_name__      ,							skip_special_tokens=__magic_name__							)

				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(__magic_name__      ,							__magic_name__							)





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


				"""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(__magic_name__      ,							__magic_name__							)
										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(__magic_name__      ,							__magic_name__							)

										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(__magic_name__							):
													lowerCAmelCase__								=							0
													lowerCAmelCase__								=							1
													lowerCAmelCase__								=							0
													lowerCAmelCase__								=							1
										lowerCAmelCase__								=							pt_model_class(__magic_name__							).eval()
										lowerCAmelCase__								=							model_class(__magic_name__      ,							dtype=jnp.floataa							)

										lowerCAmelCase__								=							convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,							__magic_name__							)
										lowerCAmelCase__								=							fx_state

										with torch.no_grad():
													lowerCAmelCase__								=							pt_model(**__magic_name__							).to_tuple()

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

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

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





	@is_pt_flax_cross_test
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict							):


				"""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(__magic_name__      ,							__magic_name__							)
										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(__magic_name__      ,							__magic_name__							)

										lowerCAmelCase__								=							pt_model_class(__magic_name__							).eval()
										lowerCAmelCase__								=							model_class(__magic_name__      ,							dtype=jnp.floataa							)

										lowerCAmelCase__								=							load_flax_weights_in_pytorch_model(__magic_name__      ,							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(__magic_name__							):
													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(**__magic_name__							).to_tuple()

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

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

										with torch.no_grad():
													lowerCAmelCase__								=							pt_model_loaded(**__magic_name__							).to_tuple()

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







	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict							):


				"""simple docstring"""

				for model_class_name in self.all_model_classes:
							lowerCAmelCase__								=							model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"							)
							lowerCAmelCase__								=							model(np.ones((1, 1)							)							)
							self.assertIsNotNone(__magic_name__							)







'''simple docstring'''

def   A     (      UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int      )					->       int:







			'''simple docstring'''



			return len_board * len_board_column * row + column







def   A     (      UpperCamelCase_					:     list[list[str]]						,	UpperCamelCase_					:     str						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     set[int]						,	)					->       bool:







			'''simple docstring'''



			if board[row][column] != word[word_index]:
						return False

			if word_index == len(UpperCamelCase_      ) - 1:
						return True

			lowerCAmelCase__								=							[(0, 1), (0, -1), (-1, 0), (1, 0)]
			lowerCAmelCase__								=							len(UpperCamelCase_      )
			lowerCAmelCase__								=							len(board[0]      )
			for direction in traverts_directions:
						lowerCAmelCase__								=							row + direction[0]
						lowerCAmelCase__								=							column + direction[1]
						if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
									continue

						lowerCAmelCase__								=							get_point_key(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_      )
						if key in visited_points_set:
									continue

						visited_points_set.add(UpperCamelCase_      )
						if exits_word(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	word_index + 1						,	UpperCamelCase_      ):
									return True

						visited_points_set.remove(UpperCamelCase_      )

			return False







def   A     (      UpperCamelCase_					:     list[list[str]]						,	UpperCamelCase_					:     str      )					->       bool:







			'''simple docstring'''



			lowerCAmelCase__								=							(
			    "The board should be a non empty matrix of single chars strings."
			)

			lowerCAmelCase__								=							len(UpperCamelCase_      )
			if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ) or len(UpperCamelCase_      ) == 0:
						raise ValueError(UpperCamelCase_      )

			for row in board:
						if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ) or len(UpperCamelCase_      ) == 0:
									raise ValueError(UpperCamelCase_      )

						for item in row:
									if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ) or len(UpperCamelCase_      ) != 1:
												raise ValueError(UpperCamelCase_      )

    # Validate word
			if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ) or len(UpperCamelCase_      ) == 0:
						raise ValueError(
						    "The word parameter should be a string of length greater than 0."      )

			lowerCAmelCase__								=							len(board[0]      )
			for i in range(UpperCamelCase_      ):
						for j in range(UpperCamelCase_      ):
									if exits_word(
									    UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	0						,	{get_point_key(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_      )}      ):
												return True

			return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()





