





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
						_SCREAMING_SNAKE_CASE   						=		"""platform"""

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a=None ,			__a=None ):
	if attention_mask is None:
		snake_case_		:      str           =						np.where(input_ids != config.pad_token_id ,			1 ,			0 )
	return {
	    "input_ids": input_ids,
	    "attention_mask": attention_mask,
	}
@require_flax
class SCREAMING_SNAKE_CASE_    :






						def __init__(   self   :   List[str]		, _A   :   Optional[int]		, _A   :   Optional[Any]=13		, _A   :   Optional[Any]=7		, _A   :   List[Any]=True		, _A   :   Any=False		, _A   :   Dict=99		, _A   :   Any=16		, _A   :   Optional[int]=2		, _A   :   Tuple=4		, _A   :   Optional[int]=4		, _A   :   int="gelu"		, _A   :   Tuple=0.1		, _A   :   Optional[Any]=0.1		, _A   :   Optional[Any]=20		, _A   :   int=2		, _A   :   str=1		, _A   :   Any=0		, _A   :   Tuple=16		, _A   :   Optional[int]=16		, _A   :   Optional[Any]=0.0_2		, )				->     List[Any]:







							"""simple docstring"""


							snake_case_		:      List[str]           =						parent
							snake_case_		:      str           =						batch_size
							snake_case_		:      str           =						seq_length
							snake_case_		:      int           =						is_training
							snake_case_		:      Optional[Any]           =						use_labels
							snake_case_		:      Tuple           =						vocab_size
							snake_case_		:      Dict           =						hidden_size
							snake_case_		:      List[str]           =						num_hidden_layers
							snake_case_		:      Optional[int]           =						num_attention_heads
							snake_case_		:      Optional[int]           =						intermediate_size
							snake_case_		:      Optional[Any]           =						hidden_act
							snake_case_		:      List[str]           =						hidden_dropout_prob
							snake_case_		:      Optional[Any]           =						attention_probs_dropout_prob
							snake_case_		:      Tuple           =						max_position_embeddings
							snake_case_		:      int           =						eos_token_id
							snake_case_		:      Optional[Any]           =						pad_token_id
							snake_case_		:      str           =						bos_token_id
							snake_case_		:      Tuple           =						embed_dim
							snake_case_		:      Dict           =						word_embed_proj_dim
							snake_case_		:      Tuple           =						initializer_range
							snake_case_		:      List[str]           =						False






						def UpperCAmelCase_					(   self   :   str						)				->     Dict:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		, self.vocab_size						)		, 3		, self.vocab_size						)
							snake_case_		:      Tuple           =						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		, dtype=np.intaa						))		, -1						)

							snake_case_		:      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		, )
							snake_case_		:      Dict           =						prepare_opt_inputs_dict(_A		, _A						)
							return config, inputs_dict






						def UpperCAmelCase_					(   self   :   List[Any]						)				->     Union[str, Any]:







							"""simple docstring"""


							snake_case_    ,snake_case_		:      Optional[Any]           =						self.prepare_config_and_inputs()
							return config, inputs_dict






						def UpperCAmelCase_					(   self   :   Union[str, Any]		, _A   :   Tuple		, _A   :   Optional[Any]		, _A   :   Dict						)				->     Any:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						20
							snake_case_		:      Optional[Any]           =						model_class_name(_A						)

							snake_case_		:      Optional[int]           =						inputs_dict['input_ids']
							snake_case_		:      List[str]           =						inputs_dict['attention_mask']

							snake_case_		:      Optional[int]           =						model.init_cache(input_ids.shape[0]		, _A						)
							snake_case_		:      Tuple           =						jnp.ones((input_ids.shape[0], max_length)		, dtype='i4'						)

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

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

							snake_case_		:      Union[str, Any]           =						model(_A						)

							snake_case_		:      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 UpperCAmelCase_					(   self   :   List[Any]		, _A   :   int		, _A   :   List[str]		, _A   :   Tuple						)				->     List[Any]:







							"""simple docstring"""


							snake_case_		:      Tuple           =						20
							snake_case_		:      int           =						model_class_name(_A						)

							snake_case_    ,snake_case_		:      List[Any]           =						(
							    inputs_dict['input_ids'],
							    inputs_dict['attention_mask'],
							)

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

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

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

							snake_case_		:      Tuple           =						model(_A		, attention_mask=_A						)

							snake_case_		:      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 SCREAMING_SNAKE_CASE_    (       snake_case_							,					unittest.TestCase							,					snake_case_       ):
						__magic_name__:      List[str]			   =							(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
						__magic_name__:      List[str]			   =							() if is_flax_available() else ()






						def UpperCAmelCase_					(   self   :   Any						)				->     Union[str, Any]:







							"""simple docstring"""


							snake_case_		:      str           =						FlaxOPTModelTester(self						)






						def UpperCAmelCase_					(   self   :   Optional[int]						)				->     List[Any]:







							"""simple docstring"""


							snake_case_    ,snake_case_		:      Union[str, 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   :   Optional[Any]						)				->     Optional[Any]:







							"""simple docstring"""


							snake_case_    ,snake_case_		:      List[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   :   int						)				->     Union[str, Any]:







							"""simple docstring"""


							for model_class_name in self.all_model_classes:
								snake_case_		:      Optional[Any]           =						model_class_name.from_pretrained('facebook/opt-125m'						)
								snake_case_		:      Dict           =						np.ones((1, 1)						) * model.config.eos_token_id
								snake_case_		:      Any           =						model(_A						)
								self.assertIsNotNone(_A						)
@require_sentencepiece
@require_flax
class SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						@slow
						def UpperCAmelCase_					(   self   :   Tuple						)				->     Optional[int]:







							"""simple docstring"""


							snake_case_		:      Optional[Any]           =						FlaxOPTModel.from_pretrained('facebook/opt-350m'						)
							snake_case_		:      Union[str, Any]           =						jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]						)
							snake_case_		:      Tuple           =						model(input_ids=_A						).last_hidden_state
							snake_case_		:      Tuple           =						(1, 11, 512)
							self.assertEqual(output.shape		, _A						)
							snake_case_		:      Union[str, Any]           =						jnp.array(
							    [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]						)
							self.assertTrue(jnp.allclose(output[:, :3, :3]		, _A		, atol=4E-2						)						)
@require_flax
@slow
class SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						def UpperCAmelCase_					(   self   :   List[str]						)				->     str:







							"""simple docstring"""


							super().setUp()
							snake_case_		:      Optional[int]           =						'facebook/opt-350m'






						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     Tuple:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						FlaxOPTForCausalLM.from_pretrained(self.path_model						)
							snake_case_		:      Dict           =						GPTaTokenizer.from_pretrained(self.path_model						)

							snake_case_		:      int           =						[
							    '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
							snake_case_		:      Union[str, Any]           =						tokenizer(_A		, return_tensors='jax'		, padding=_A		, add_special_tokens=_A						)
							snake_case_		:      List[str]           =						model(inputs.input_ids		, attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
							snake_case_		:      Union[str, Any]           =						jnp.array(
							    [
							        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
							        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
							        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
							        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
							    ]						)
							self.assertTrue(jnp.allclose(_A		, _A		, atol=4E-2						)						)

							snake_case_		:      Any           =						jax.jit(_A						)
							snake_case_		:      Tuple           =						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 SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						@property
						def UpperCAmelCase_					(   self   :   List[str]						)				->     int:







							"""simple docstring"""


							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   :   str						)				->     Dict:







							"""simple docstring"""


							snake_case_		:      List[str]           =						'facebook/opt-125m'

							snake_case_		:      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',
							]

							snake_case_		:      Optional[int]           =						[]

							snake_case_		:      Tuple           =						FlaxOPTForCausalLM.from_pretrained(_A						)
							snake_case_		:      int           =						GPTaTokenizer.from_pretrained(_A						)

							for prompt in self.prompts:
								snake_case_		:      Any           =						tokenizer(_A		, return_tensors='jax'						).input_ids

								snake_case_		:      List[str]           =						model.generate(_A		, max_length=10						)
								snake_case_		:      str           =						generated_ids[0]

								snake_case_		:      Dict           =						tokenizer.batch_decode(_A		, skip_special_tokens=_A						)
								predicted_outputs += generated_string

							self.assertListEqual(_A		, _A						)






						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     int:







							"""simple docstring"""


							snake_case_		:      int           =						'facebook/opt-350m'

							snake_case_		:      Dict           =						[
							    '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',
							]

							snake_case_		:      str           =						[]
							snake_case_		:      List[str]           =						FlaxOPTForCausalLM.from_pretrained(_A						)
							snake_case_		:      Tuple           =						GPTaTokenizer.from_pretrained(_A						)

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

								snake_case_		:      Dict           =						model.generate(_A		, max_length=10						)
								snake_case_		:      str           =						generated_ids[0]

								snake_case_		:      int           =						tokenizer.batch_decode(_A		, skip_special_tokens=_A						)
								predicted_outputs += generated_string

							self.assertListEqual(_A		, _A						)






						def UpperCAmelCase_					(   self   :   int						)				->     Optional[int]:







							"""simple docstring"""


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

							snake_case_		:      Any           =						jax.jit(model.generate						)

							snake_case_		:      int           =						jit_generate(inputs['input_ids']		, attention_mask=inputs['attention_mask']						).sequences

							snake_case_		:      Dict           =						tokenizer.batch_decode(_A		, skip_special_tokens=_A						)

							self.assertIsNotNone(_A		, _A						)






						def UpperCAmelCase_					(   self   :   int						)				->     Optional[int]:







							"""simple docstring"""


							snake_case_		:      str           =						'facebook/opt-350m'

							snake_case_		:      Any           =						GPTaTokenizer.from_pretrained(_A						)
							snake_case_		:      List[str]           =						FlaxOPTForCausalLM.from_pretrained(_A						)

							snake_case_		:      int           =						'left'

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

							snake_case_		:      Optional[int]           =						tokenizer(_A		, return_tensors='jax'		, padding=_A						)
							snake_case_		:      Dict           =						inputs['input_ids']

							snake_case_		:      List[str]           =						model.generate(input_ids=_A		, attention_mask=inputs['attention_mask']		, trace=_A						)

							snake_case_		:      Optional[int]           =						tokenizer(sentences[0]		, return_tensors='jax'						).input_ids
							snake_case_		:      Optional[int]           =						model.generate(input_ids=_A						)

							snake_case_		:      Tuple           =						inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
							snake_case_		:      List[str]           =						tokenizer(sentences[1]		, return_tensors='jax'						).input_ids
							snake_case_		:      Optional[int]           =						model.generate(input_ids=_A		, max_length=model.config.max_length - num_paddings						)

							snake_case_		:      int           =						tokenizer.batch_decode(outputs[0]		, skip_special_tokens=_A						)
							snake_case_		:      Union[str, Any]           =						tokenizer.decode(output_non_padded[0][0]		, skip_special_tokens=_A						)
							snake_case_		:      Dict           =						tokenizer.decode(output_padded[0][0]		, skip_special_tokens=_A						)

							snake_case_		:      Union[str, Any]           =						[
							    '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]						)








_SCREAMING_SNAKE_CASE   						=		{
    0: """underwear""",
    1: """pants""",
    2: """belt""",
    3: """suit""",
    4: """shoe""",
    5: """socks""",
    6: """shirt""",
    7: """tie""",
    8: """watch""",
}

_SCREAMING_SNAKE_CASE   						=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_SCREAMING_SNAKE_CASE   						=		[0 for x in range(len(graph))]
_SCREAMING_SNAKE_CASE   						=		[]


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_		:      Any           =						1
	while stack:
		snake_case_		:      List[Any]           =						stack.pop()
		print(__a ,			clothes[current_clothing] )
		order += 1


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ):
	snake_case_		:      int           =						1
	for v in graph[u]:
		if not visited[v]:
			depth_first_search(__a ,			__a ,			__a )

	stack.append(__a )


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	for v in range(len(__a ) ):
		if not visited[v]:
			depth_first_search(__a ,			__a ,			__a )


if __name__ == "__main__":
						topological_sort(graph, visited)
						print(stack)
						print_stack(stack, clothes)

