






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  __UpperCAmelCase										=		"platform"

  import jax
  import jax.numpy as jnp

  from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def   lowercase__   (  __snake_case					:							Any  , __snake_case					:							List[Any]  , __snake_case					:							List[Any]=None  , __snake_case					:							Dict=None						):


     '''simple docstring'''


     if attention_mask is None:
          UpperCAmelCase_					:   str   =		np.where(input_ids != config.pad_token_id  , 1  , 0						)
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }



@require_flax
class 						lowerCamelCase   :







  '''simple docstring'''



  def __init__(							self       ,	_UpperCamelCase       ,	_UpperCamelCase=1_3       ,	_UpperCamelCase=7       ,	_UpperCamelCase=True       ,	_UpperCamelCase=False       ,	_UpperCamelCase=9_9       ,	_UpperCamelCase=1_6       ,	_UpperCamelCase=2       ,	_UpperCamelCase=4       ,	_UpperCamelCase=4       ,	_UpperCamelCase="gelu"       ,	_UpperCamelCase=0.1       ,	_UpperCamelCase=0.1       ,	_UpperCamelCase=2_0       ,	_UpperCamelCase=2       ,	_UpperCamelCase=1       ,	_UpperCamelCase=0       ,	_UpperCamelCase=1_6       ,	_UpperCamelCase=1_6       ,	_UpperCamelCase=0.02       ,	)			->							Optional[int]:
       UpperCAmelCase_					:   Union[str, Any]   =		parent
       UpperCAmelCase_					:   List[str]   =		batch_size
       UpperCAmelCase_					:   Dict   =		seq_length
       UpperCAmelCase_					:   Dict   =		is_training
       UpperCAmelCase_					:   Any   =		use_labels
       UpperCAmelCase_					:   Optional[int]   =		vocab_size
       UpperCAmelCase_					:   List[Any]   =		hidden_size
       UpperCAmelCase_					:   Optional[int]   =		num_hidden_layers
       UpperCAmelCase_					:   str   =		num_attention_heads
       UpperCAmelCase_					:   Dict   =		intermediate_size
       UpperCAmelCase_					:   List[str]   =		hidden_act
       UpperCAmelCase_					:   str   =		hidden_dropout_prob
       UpperCAmelCase_					:   Optional[int]   =		attention_probs_dropout_prob
       UpperCAmelCase_					:   List[str]   =		max_position_embeddings
       UpperCAmelCase_					:   Optional[int]   =		eos_token_id
       UpperCAmelCase_					:   str   =		pad_token_id
       UpperCAmelCase_					:   Dict   =		bos_token_id
       UpperCAmelCase_					:   Optional[Any]   =		embed_dim
       UpperCAmelCase_					:   Dict   =		word_embed_proj_dim
       UpperCAmelCase_					:   str   =		initializer_range
       UpperCAmelCase_					:   Union[str, Any]   =		False



  def       __UpperCAmelCase    (							self     )			->							int:
       UpperCAmelCase_					:   Tuple   =		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,	self.vocab_size     )       ,	3       ,	self.vocab_size     )
       UpperCAmelCase_					:   Union[str, Any]   =		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,	dtype=np.intaa     ))       ,	-1     )

       UpperCAmelCase_					:   Optional[Any]   =		OPTConfig(
           vocab_size=self.vocab_size       ,	hidden_size=self.hidden_size       ,	num_hidden_layers=self.num_hidden_layers       ,	num_attention_heads=self.num_attention_heads       ,	ffn_dim=self.intermediate_size       ,	dropout=self.hidden_dropout_prob       ,	attention_dropout=self.attention_probs_dropout_prob       ,	max_position_embeddings=self.max_position_embeddings       ,	eos_token_id=self.eos_token_id       ,	bos_token_id=self.bos_token_id       ,	pad_token_id=self.pad_token_id       ,	embed_dim=self.embed_dim       ,	is_encoder_decoder=a_       ,	word_embed_proj_dim=self.word_embed_proj_dim       ,	initializer_range=self.initializer_range       ,	use_cache=a_       ,	)
       UpperCAmelCase_					:   Any   =		prepare_opt_inputs_dict(a_       ,	a_     )
       return config, inputs_dict



  def       __UpperCAmelCase    (							self     )			->							Optional[Any]:
       UpperCAmelCase_ ,						UpperCAmelCase_					:   List[Any]   =		self.prepare_config_and_inputs()
       return config, inputs_dict



  def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase     )			->							str:
       UpperCAmelCase_					:   Optional[int]   =		2_0
       UpperCAmelCase_					:   str   =		model_class_name(a_     )

       UpperCAmelCase_					:   Optional[int]   =		inputs_dict['input_ids']
       UpperCAmelCase_					:   int   =		inputs_dict['attention_mask']

       UpperCAmelCase_					:   Optional[Any]   =		model.init_cache(input_ids.shape[0]       ,	a_     )
       UpperCAmelCase_					:   int   =		jnp.ones((input_ids.shape[0], max_length)       ,	dtype='i4'     )

       UpperCAmelCase_					:   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=a_       ,	past_key_values=a_       ,	position_ids=a_       ,	)

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

       UpperCAmelCase_					:   str   =		model(a_     )

       UpperCAmelCase_					:   Dict   =		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       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase     )			->							List[Any]:
       UpperCAmelCase_					:   List[str]   =		2_0
       UpperCAmelCase_					:   List[str]   =		model_class_name(a_     )

       UpperCAmelCase_ ,						UpperCAmelCase_					:   int   =		(
           inputs_dict['input_ids'],
           inputs_dict['attention_mask'],
       )

       UpperCAmelCase_					:   List[Any]   =		jnp.concatenate(
           [
               attention_mask,
               jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])     ),
           ]       ,	axis=-1       ,	)

       UpperCAmelCase_					:   List[str]   =		model.init_cache(input_ids.shape[0]       ,	a_     )
       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=a_       ,	past_key_values=a_       ,	position_ids=a_       ,	)
       UpperCAmelCase_					:   str   =		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,	dtype='i4'     )
       UpperCAmelCase_					:   Tuple   =		model(
           input_ids[:, -1:]       ,	past_key_values=outputs_cache.past_key_values       ,	attention_mask=a_       ,	position_ids=a_       ,	)

       UpperCAmelCase_					:   List[str]   =		model(a_       ,	attention_mask=a_     )

       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 						lowerCamelCase   (SCREAMING_SNAKE_CASE__				,						unittest.TestCase				,						SCREAMING_SNAKE_CASE__    ):







  '''simple docstring'''



  _snake_case					:					str 		=						(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  _snake_case					:					Optional[int] 		=						() if is_flax_available() else ()



  def       __UpperCAmelCase    (							self     )			->							List[str]:
       UpperCAmelCase_					:   List[Any]   =		FlaxOPTModelTester(self     )



  def       __UpperCAmelCase    (							self     )			->							int:
       UpperCAmelCase_ ,						UpperCAmelCase_					:   Optional[Any]   =		self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(a_       ,	a_       ,	a_     )



  def       __UpperCAmelCase    (							self     )			->							List[str]:
       UpperCAmelCase_ ,						UpperCAmelCase_					:   str   =		self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward_with_attn_mask(a_       ,	a_       ,	a_     )



  @slow
  def       __UpperCAmelCase    (							self     )			->							List[str]:



       for model_class_name in self.all_model_classes:
            UpperCAmelCase_					:   Any   =		model_class_name.from_pretrained('facebook/opt-125m'     )
            UpperCAmelCase_					:   Tuple   =		np.ones((1, 1)     ) * model.config.eos_token_id
            UpperCAmelCase_					:   int   =		model(a_     )
            self.assertIsNotNone(a_     )





@require_sentencepiece
@require_flax
class 						lowerCamelCase   (unittest.TestCase    ):







  '''simple docstring'''



  @slow
  def       __UpperCAmelCase    (							self     )			->							int:
       UpperCAmelCase_					:   List[str]   =		FlaxOPTModel.from_pretrained('facebook/opt-350m'     )
       UpperCAmelCase_					:   Union[str, Any]   =		jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]     )
       UpperCAmelCase_					:   Tuple   =		model(input_ids=a_     ).last_hidden_state
       UpperCAmelCase_					:   Union[str, Any]   =		(1, 1_1, 5_1_2)
       self.assertEqual(output.shape       ,	a_     )
       UpperCAmelCase_					:   int   =		jnp.array(
           [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]     )
       self.assertTrue(jnp.allclose(output[:, :3, :3]       ,	a_       ,	atol=4E-2     )     )





@require_flax
@slow
class 						lowerCamelCase   (unittest.TestCase    ):







  '''simple docstring'''



  def       __UpperCAmelCase    (							self     )			->							str:
       super().setUp()
       UpperCAmelCase_					:   Tuple   =		'facebook/opt-350m'



  def       __UpperCAmelCase    (							self     )			->							int:
       UpperCAmelCase_					:   int   =		FlaxOPTForCausalLM.from_pretrained(self.path_model     )
       UpperCAmelCase_					:   Optional[Any]   =		GPTaTokenizer.from_pretrained(self.path_model     )

       UpperCAmelCase_					:   str   =		[
           'Today is a beautiful day and I want to',
           'In the city of',
           'Paris is the capital of France and',
           'Computers and mobile phones have taken',
       ]
       # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
       UpperCAmelCase_					:   int   =		tokenizer(a_       ,	return_tensors='jax'       ,	padding=a_       ,	add_special_tokens=a_     )
       UpperCAmelCase_					:   Dict   =		model(inputs.input_ids       ,	attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
       UpperCAmelCase_					:   List[Any]   =		jnp.array(
           [
               [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
               [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
               [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
               [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
           ]     )
       self.assertTrue(jnp.allclose(a_       ,	a_       ,	atol=4E-2     )     )

       UpperCAmelCase_					:   Any   =		jax.jit(a_     )
       UpperCAmelCase_					:   Optional[Any]   =		model(inputs.input_ids       ,	attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
       self.assertTrue(jnp.allclose(a_       ,	a_       ,	atol=4E-2     )     )





@require_flax
@slow
class 						lowerCamelCase   (unittest.TestCase    ):







  '''simple docstring'''



  @property
  def       __UpperCAmelCase    (							self     )			->							List[str]:
       return [
           "Today is a beautiful day and I want",
           "In the city of",
           "Paris is the capital of France and",
           "Computers and mobile phones have taken",
       ]



  def       __UpperCAmelCase    (							self     )			->							List[str]:
       UpperCAmelCase_					:   Optional[int]   =		'facebook/opt-125m'

       UpperCAmelCase_					:   Tuple   =		[
           'Today is a beautiful day and I want to',
           'In the city of New York, the city',
           'Paris is the capital of France and the capital',
           'Computers and mobile phones have taken over the',
       ]

       UpperCAmelCase_					:   str   =		[]

       UpperCAmelCase_					:   Dict   =		FlaxOPTForCausalLM.from_pretrained(a_     )
       UpperCAmelCase_					:   List[str]   =		GPTaTokenizer.from_pretrained(a_     )

       for prompt in self.prompts:
            UpperCAmelCase_					:   Union[str, Any]   =		tokenizer(a_       ,	return_tensors='jax'     ).input_ids

            UpperCAmelCase_					:   List[str]   =		model.generate(a_       ,	max_length=1_0     )
            UpperCAmelCase_					:   Optional[int]   =		generated_ids[0]

            UpperCAmelCase_					:   Optional[int]   =		tokenizer.batch_decode(a_       ,	skip_special_tokens=a_     )
            predicted_outputs += generated_string

       self.assertListEqual(a_       ,	a_     )



  def       __UpperCAmelCase    (							self     )			->							List[str]:
       UpperCAmelCase_					:   Dict   =		'facebook/opt-350m'

       UpperCAmelCase_					:   Optional[Any]   =		[
           'Today is a beautiful day and I want to',
           'In the city of San Francisco, the city',
           'Paris is the capital of France and the capital',
           'Computers and mobile phones have taken over the',
       ]

       UpperCAmelCase_					:   Dict   =		[]
       UpperCAmelCase_					:   List[Any]   =		FlaxOPTForCausalLM.from_pretrained(a_     )
       UpperCAmelCase_					:   Any   =		GPTaTokenizer.from_pretrained(a_     )

       for prompt in self.prompts:
            UpperCAmelCase_					:   List[str]   =		tokenizer(a_       ,	return_tensors='jax'     ).input_ids

            UpperCAmelCase_					:   Optional[int]   =		model.generate(a_       ,	max_length=1_0     )
            UpperCAmelCase_					:   Dict   =		generated_ids[0]

            UpperCAmelCase_					:   str   =		tokenizer.batch_decode(a_       ,	skip_special_tokens=a_     )
            predicted_outputs += generated_string

       self.assertListEqual(a_       ,	a_     )



  def       __UpperCAmelCase    (							self     )			->							List[Any]:
       UpperCAmelCase_					:   Optional[int]   =		'facebook/opt-125m'
       UpperCAmelCase_					:   Any   =		[
           'Today is a beautiful day and I want to thank',
           'In the city of Rome Canaver Canaver Canaver Canaver',
       ]
       UpperCAmelCase_					:   Union[str, Any]   =		FlaxOPTForCausalLM.from_pretrained(a_     )
       UpperCAmelCase_					:   List[Any]   =		GPTaTokenizer.from_pretrained(a_     )
       UpperCAmelCase_					:   Union[str, Any]   =		tokenizer(
           [
               'Today is a beautiful day and I want to',
               'In the city of',
           ]       ,	return_tensors='jax'       ,	padding=a_       ,	)

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

       UpperCAmelCase_					:   Tuple   =		jit_generate(inputs['input_ids']       ,	attention_mask=inputs['attention_mask']     ).sequences

       UpperCAmelCase_					:   str   =		tokenizer.batch_decode(a_       ,	skip_special_tokens=a_     )

       self.assertIsNotNone(a_       ,	a_     )



  def       __UpperCAmelCase    (							self     )			->							Dict:
       UpperCAmelCase_					:   Optional[int]   =		'facebook/opt-350m'

       UpperCAmelCase_					:   Any   =		GPTaTokenizer.from_pretrained(a_     )
       UpperCAmelCase_					:   Any   =		FlaxOPTForCausalLM.from_pretrained(a_     )

       UpperCAmelCase_					:   Tuple   =		'left'

       # use different length sentences to test batching
       UpperCAmelCase_					:   Union[str, Any]   =		[
           'Hello, my dog is a little',
           'Today, I',
       ]

       UpperCAmelCase_					:   Dict   =		tokenizer(a_       ,	return_tensors='jax'       ,	padding=a_     )
       UpperCAmelCase_					:   str   =		inputs['input_ids']

       UpperCAmelCase_					:   int   =		model.generate(input_ids=a_       ,	attention_mask=inputs['attention_mask']       ,	trace=a_     )

       UpperCAmelCase_					:   Optional[int]   =		tokenizer(sentences[0]       ,	return_tensors='jax'     ).input_ids
       UpperCAmelCase_					:   Any   =		model.generate(input_ids=a_     )

       UpperCAmelCase_					:   Dict   =		inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
       UpperCAmelCase_					:   List[Any]   =		tokenizer(sentences[1]       ,	return_tensors='jax'     ).input_ids
       UpperCAmelCase_					:   List[str]   =		model.generate(input_ids=a_       ,	max_length=model.config.max_length - num_paddings     )

       UpperCAmelCase_					:   List[str]   =		tokenizer.batch_decode(outputs[0]       ,	skip_special_tokens=a_     )
       UpperCAmelCase_					:   Union[str, Any]   =		tokenizer.decode(output_non_padded[0][0]       ,	skip_special_tokens=a_     )
       UpperCAmelCase_					:   Union[str, Any]   =		tokenizer.decode(output_padded[0][0]       ,	skip_special_tokens=a_     )

       UpperCAmelCase_					:   Dict   =		[
           'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
           'Today, I was in the middle of a conversation with a friend about the',
       ]
       self.assertListEqual(a_       ,	a_     )
       self.assertListEqual(a_       ,	[non_padded_sentence, padded_sentence]     )














def   lowercase__   (  __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int						):


					'''simple docstring'''


					return len_board * len_board_column * row + column




def   lowercase__   (  __snake_case					:							list[list[str]]  , __snake_case					:							str  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							set[int]  , ):


					'''simple docstring'''


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

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

					UpperCAmelCase_					:   Dict   =		[(0, 1), (0, -1), (-1, 0), (1, 0)]
					UpperCAmelCase_					:   Union[str, Any]   =		len(__snake_case						)
					UpperCAmelCase_					:   List[Any]   =		len(board[0]						)
					for direction in traverts_directions:
										UpperCAmelCase_					:   Optional[int]   =		row + direction[0]
										UpperCAmelCase_					:   Optional[int]   =		column + direction[1]
										if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
															continue

										UpperCAmelCase_					:   Tuple   =		get_point_key(__snake_case  , __snake_case  , __snake_case  , __snake_case						)
										if key in visited_points_set:
															continue

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

										visited_points_set.remove(__snake_case						)

					return False




def   lowercase__   (  __snake_case					:							list[list[str]]  , __snake_case					:							str						):


					'''simple docstring'''


					UpperCAmelCase_					:   Any   =		(
					    'The board should be a non empty matrix of single chars strings.'
					)

					UpperCAmelCase_					:   Optional[Any]   =		len(__snake_case						)
					if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
										raise ValueError(__snake_case						)

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

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

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

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

					return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()






