
"""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 							__magic_name__     :





					def __init__(				self   :							Union[str, Any]							,						snake_case_   :							List[Any]							,						snake_case_   :							Any=14							,						snake_case_   :							List[str]=7							,						snake_case_   :							List[str]=True							,						snake_case_   :							List[Any]=True							,						snake_case_   :							Optional[int]=False							,						snake_case_   :							Optional[Any]=True							,						snake_case_   :							Any=99							,						snake_case_   :							Union[str, Any]=32							,						snake_case_   :							Optional[Any]=4							,						snake_case_   :							Optional[Any]=4							,						snake_case_   :							str=4							,						snake_case_   :							str=37							,						snake_case_   :							Union[str, Any]="gelu"							,						snake_case_   :							Optional[Any]=0.1							,						snake_case_   :							Union[str, Any]=0.1							,						snake_case_   :							str=512							,						snake_case_   :							Optional[Any]=0.02							,						):
												__snake_case     			=       parent
												__snake_case     			=       batch_size
												__snake_case     			=       seq_length
												__snake_case     			=       is_training
												__snake_case     			=       use_input_mask
												__snake_case     			=       use_token_type_ids
												__snake_case     			=       use_labels
												__snake_case     			=       vocab_size
												__snake_case     			=       hidden_size
												__snake_case     			=       rotary_dim
												__snake_case     			=       num_hidden_layers
												__snake_case     			=       num_attention_heads
												__snake_case     			=       intermediate_size
												__snake_case     			=       hidden_act
												__snake_case     			=       hidden_dropout_prob
												__snake_case     			=       attention_probs_dropout_prob
												__snake_case     			=       max_position_embeddings
												__snake_case     			=       initializer_range
												__snake_case     			=       None
												__snake_case     			=       vocab_size - 1
												__snake_case     			=       vocab_size - 1
												__snake_case     			=       vocab_size - 1





					def 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case     			=       ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size    )

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

												__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=snake_case_							,						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 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       self.prepare_config_and_inputs()
												__snake_case			,   __snake_case			,   __snake_case     			=       config_and_inputs
												__snake_case     			=       {"input_ids": input_ids, "attention_mask": attention_mask}
												return config, inputs_dict





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Dict							,						snake_case_   :							Dict							,						snake_case_   :							Any							,						snake_case_   :							List[Any]    ):
												__snake_case     			=       20
												__snake_case     			=       model_class_name(snake_case_    )

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

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

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

												__snake_case     			=       model(snake_case_    )

												__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 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Optional[int]							,						snake_case_   :							List[Any]							,						snake_case_   :							List[Any]    ):
												__snake_case     			=       20
												__snake_case     			=       model_class_name(snake_case_    )

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

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

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

												__snake_case     			=       model(snake_case_							,						attention_mask=snake_case_    )

												__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 							__magic_name__     (		lowercase__      ,		lowercase__      ,		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					int     							=   (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   (FlaxGPTJForCausalLM,) if is_flax_available() else ()





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       FlaxGPTJModelTester(self    )





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												for model_class_name in self.all_model_classes:
																			__snake_case			,   __snake_case			,   __snake_case     			=       self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												for model_class_name in self.all_model_classes:
																			__snake_case			,   __snake_case			,   __snake_case     			=       self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward_with_attn_mask(
																			    snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )





					@tooslow
					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       GPTaTokenizer.from_pretrained("gpt2"							,						pad_token="<|endoftext|>"							,						padding_side="left"    )
												__snake_case     			=       tokenizer(["Hello this is a long string", "Hey"]							,						return_tensors="np"							,						padding=snake_case_							,						truncation=snake_case_    )

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

												__snake_case     			=       jax.jit(model.generate    )

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

												__snake_case     			=       tokenizer.batch_decode(snake_case_							,						skip_special_tokens=snake_case_    )

												__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(snake_case_							,						snake_case_    )





					@is_pt_flax_cross_test
					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												__snake_case			,   __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
																										__snake_case     			=       self._prepare_for_class(snake_case_							,						snake_case_    )
																										__snake_case     			=       {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

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

																										__snake_case     			=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,						snake_case_    )
																										__snake_case     			=       fx_state

																										with torch.no_grad():
																																	__snake_case     			=       pt_model(**snake_case_    ).to_tuple()

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

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

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





					@is_pt_flax_cross_test
					def 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case			,   __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
																										__snake_case     			=       self._prepare_for_class(snake_case_							,						snake_case_    )
																										__snake_case     			=       {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

																										__snake_case     			=       pt_model_class(snake_case_    ).eval()
																										__snake_case     			=       model_class(snake_case_							,						dtype=jnp.floataa    )

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

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

																										with torch.no_grad():
																																	__snake_case     			=       pt_model(**snake_case_    ).to_tuple()

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

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

																										with torch.no_grad():
																																	__snake_case     			=       pt_model_loaded(**snake_case_    ).to_tuple()

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





					@tooslow
					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												for model_class_name in self.all_model_classes:
																			__snake_case     			=       model_class_name.from_pretrained("EleutherAI/gpt-j-6B"    )
																			__snake_case     			=       model(np.ones((1, 1)    )    )
																			self.assertIsNotNone(snake_case_    )


"""simple docstring"""







from heapq import heappop, heappush

import numpy as np
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    )  ->     tuple[float | int, list[tuple[int, int]]]:







							"""simple docstring"""


							__snake_case			,   __snake_case     			=       grid.shape
							__snake_case     			=       [-1, 1, 0, 0]
							__snake_case     			=       [0, 0, -1, 1]
							if allow_diagonal:
														dx += [-1, -1, 1, 1]
														dy += [-1, 1, -1, 1]

							__snake_case			,   __snake_case     			=       [(0, source)], set()
							__snake_case     			=       np.full((rows, cols)							,    np.inf )
							__snake_case     			=       0
							__snake_case     			=       np.empty((rows, cols)							,    dtype=SCREAMING_SNAKE_CASE )
							__snake_case     			=       None

							while queue:
														((__snake_case)			,   (__snake_case))     			=       heappop(SCREAMING_SNAKE_CASE )
														if (x, y) in visited:
																					continue
														visited.add((x, y) )

														if (x, y) == destination:
																					__snake_case     			=       []
																					while (x, y) != source:
																												path.append((x, y) )
																												__snake_case			,   __snake_case     			=       predecessors[x, y]
																					path.append(SCREAMING_SNAKE_CASE )  # add the source manually
																					path.reverse()
																					return matrix[destination], path

														for i in range(len(SCREAMING_SNAKE_CASE ) ):
																					__snake_case			,   __snake_case     			=       x + dx[i], y + dy[i]
																					if 0 <= nx < rows and 0 <= ny < cols:
																												__snake_case     			=       grid[nx][ny]
																												if next_node == 1 and matrix[nx, ny] > dist + 1:
																																			heappush(SCREAMING_SNAKE_CASE							,    (dist + 1, (nx, ny)) )
																																			__snake_case     			=       dist + 1
																																			__snake_case     			=       (x, y)

							return np.inf, []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
