
"""simple docstring"""
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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 MarianTokenizer
			from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase=None				,  _lowerCAmelCase=None				,  _lowerCAmelCase=None				,  _lowerCAmelCase=None				,  _lowerCAmelCase=None				,  _lowerCAmelCase=None				,  ):





						if attention_mask is None:
												__lowerCAmelCase        =       np.where(input_ids != config.pad_token_id				,  1				,  0		)
						if decoder_attention_mask is None:
												__lowerCAmelCase        =       np.where(decoder_input_ids != config.pad_token_id				,  1				,  0		)
						if head_mask is None:
												__lowerCAmelCase        =       np.ones((config.encoder_layers, config.encoder_attention_heads)		)
						if decoder_head_mask is None:
												__lowerCAmelCase        =       np.ones((config.decoder_layers, config.decoder_attention_heads)		)
						if cross_attn_head_mask is None:
												__lowerCAmelCase        =       np.ones((config.decoder_layers, config.decoder_attention_heads)		)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						}
class      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self  ,							snake_case_  ,							snake_case_=13  ,							snake_case_=7  ,							snake_case_=True  ,							snake_case_=False  ,							snake_case_=99  ,							snake_case_=16  ,							snake_case_=2  ,							snake_case_=4  ,							snake_case_=4  ,							snake_case_="gelu"  ,							snake_case_=0.1  ,							snake_case_=0.1  ,							snake_case_=32  ,							snake_case_=2  ,							snake_case_=1  ,							snake_case_=0  ,							snake_case_=0.02  ,							)   ->       Optional[Any]:
													__lowerCAmelCase        =       parent
													__lowerCAmelCase        =       batch_size
													__lowerCAmelCase        =       seq_length
													__lowerCAmelCase        =       is_training
													__lowerCAmelCase        =       use_labels
													__lowerCAmelCase        =       vocab_size
													__lowerCAmelCase        =       hidden_size
													__lowerCAmelCase        =       num_hidden_layers
													__lowerCAmelCase        =       num_attention_heads
													__lowerCAmelCase        =       intermediate_size
													__lowerCAmelCase        =       hidden_act
													__lowerCAmelCase        =       hidden_dropout_prob
													__lowerCAmelCase        =       attention_probs_dropout_prob
													__lowerCAmelCase        =       max_position_embeddings
													__lowerCAmelCase        =       eos_token_id
													__lowerCAmelCase        =       pad_token_id
													__lowerCAmelCase        =       bos_token_id
													__lowerCAmelCase        =       initializer_range




							def  A__		(				self							)   ->       str:
													__lowerCAmelCase        =       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,							self.vocab_size							)  ,							3  ,							self.vocab_size							)
													__lowerCAmelCase        =       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,							dtype=np.intaa							))  ,							-1							)

													__lowerCAmelCase        =       shift_tokens_right(snake_case_  ,							1  ,							2							)

													__lowerCAmelCase        =       MarianConfig(
													    vocab_size=self.vocab_size  ,							d_model=self.hidden_size  ,							encoder_layers=self.num_hidden_layers  ,							decoder_layers=self.num_hidden_layers  ,							encoder_attention_heads=self.num_attention_heads  ,							decoder_attention_heads=self.num_attention_heads  ,							encoder_ffn_dim=self.intermediate_size  ,							decoder_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  ,							initializer_range=self.initializer_range  ,							use_cache=snake_case_  ,							)
													__lowerCAmelCase        =       prepare_marian_inputs_dict(snake_case_  ,							snake_case_  ,							snake_case_							)
													return config, inputs_dict




							def  A__		(				self							)   ->       Any:
													__lowerCAmelCase ,      __lowerCAmelCase        =       self.prepare_config_and_inputs()
													return config, inputs_dict




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_							)   ->       Optional[int]:
													__lowerCAmelCase        =       20
													__lowerCAmelCase        =       model_class_name(snake_case_							)

													__lowerCAmelCase        =       model.encode(inputs_dict["""input_ids"""]							)

													__lowerCAmelCase ,      __lowerCAmelCase        =       (
													    inputs_dict["""decoder_input_ids"""],
													    inputs_dict["""decoder_attention_mask"""],
													)

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

													__lowerCAmelCase        =       jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]  ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,							)
													__lowerCAmelCase        =       model.decode(
													    decoder_input_ids[:, :-1]  ,							snake_case_  ,							decoder_attention_mask=snake_case_  ,							past_key_values=snake_case_  ,							decoder_position_ids=snake_case_  ,							)

													__lowerCAmelCase        =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,							dtype="""i4"""							)
													__lowerCAmelCase        =       model.decode(
													    decoder_input_ids[:, -1:]  ,							snake_case_  ,							decoder_attention_mask=snake_case_  ,							past_key_values=outputs_cache.past_key_values  ,							decoder_position_ids=snake_case_  ,							)

													__lowerCAmelCase        =       model.decode(snake_case_  ,							snake_case_							)

													__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  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_							)   ->       str:
													__lowerCAmelCase        =       20
													__lowerCAmelCase        =       model_class_name(snake_case_							)

													__lowerCAmelCase        =       model.encode(inputs_dict["""input_ids"""]							)

													__lowerCAmelCase ,      __lowerCAmelCase        =       (
													    inputs_dict["""decoder_input_ids"""],
													    inputs_dict["""decoder_attention_mask"""],
													)

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

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

													__lowerCAmelCase        =       model.decode(
													    decoder_input_ids[:, :-1]  ,							snake_case_  ,							decoder_attention_mask=snake_case_  ,							past_key_values=snake_case_  ,							decoder_position_ids=snake_case_  ,							)
													__lowerCAmelCase        =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,							dtype="""i4"""							)
													__lowerCAmelCase        =       model.decode(
													    decoder_input_ids[:, -1:]  ,							snake_case_  ,							past_key_values=outputs_cache.past_key_values  ,							decoder_attention_mask=snake_case_  ,							decoder_position_ids=snake_case_  ,							)

													__lowerCAmelCase        =       model.decode(snake_case_  ,							snake_case_  ,							decoder_attention_mask=snake_case_							)

													__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      lowerCAmelCase_						(							A__							,							unittest.TestCase							,							A__				):



							'''simple docstring'''





							_snake_case		     =						True
							_snake_case		     =						(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
							_snake_case		     =						(FlaxMarianMTModel,) if is_flax_available() else ()




							def  A__		(				self							)   ->       Optional[Any]:
													__lowerCAmelCase        =       FlaxMarianModelTester(self							)




							def  A__		(				self							)   ->       List[Any]:
													__lowerCAmelCase ,      __lowerCAmelCase        =       self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward(snake_case_  ,							snake_case_  ,							snake_case_							)




							def  A__		(				self							)   ->       Dict:
													__lowerCAmelCase ,      __lowerCAmelCase        =       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(snake_case_  ,							snake_case_  ,							snake_case_							)




							def  A__		(				self							)   ->       List[Any]:
													__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__							):
																									__lowerCAmelCase        =       self._prepare_for_class(snake_case_  ,							snake_case_							)
																									__lowerCAmelCase        =       model_class(snake_case_							)

																									@jax.jit
																									def encode_jitted(snake_case_  ,							snake_case_=None  ,							**snake_case_							):
																															return model.encode(input_ids=snake_case_  ,							attention_mask=snake_case_							)

																									with self.subTest("""JIT Enabled"""							):
																															__lowerCAmelCase        =       encode_jitted(**snake_case_							).to_tuple()

																									with self.subTest("""JIT Disabled"""							):
																															with jax.disable_jit():
																																					__lowerCAmelCase        =       encode_jitted(**snake_case_							).to_tuple()

																									self.assertEqual(len(snake_case_							)  ,							len(snake_case_							)							)
																									for jitted_output, output in zip(snake_case_  ,							snake_case_							):
																															self.assertEqual(jitted_output.shape  ,							output.shape							)




							def  A__		(				self							)   ->       Tuple:
													__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__							):
																									__lowerCAmelCase        =       model_class(snake_case_							)
																									__lowerCAmelCase        =       model.encode(inputs_dict["""input_ids"""]  ,							inputs_dict["""attention_mask"""]							)

																									__lowerCAmelCase        =       {
																									    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																									    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																									    """encoder_outputs""": encoder_outputs,
																									}

																									@jax.jit
																									def decode_jitted(snake_case_  ,							snake_case_  ,							snake_case_							):
																															return model.decode(
																															    decoder_input_ids=snake_case_  ,							decoder_attention_mask=snake_case_  ,							encoder_outputs=snake_case_  ,							)

																									with self.subTest("""JIT Enabled"""							):
																															__lowerCAmelCase        =       decode_jitted(**snake_case_							).to_tuple()

																									with self.subTest("""JIT Disabled"""							):
																															with jax.disable_jit():
																																					__lowerCAmelCase        =       decode_jitted(**snake_case_							).to_tuple()

																									self.assertEqual(len(snake_case_							)  ,							len(snake_case_							)							)
																									for jitted_output, output in zip(snake_case_  ,							snake_case_							):
																															self.assertEqual(jitted_output.shape  ,							output.shape							)




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






													for model_class_name in self.all_model_classes:
																			__lowerCAmelCase        =       model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""							)
																			# FlaxMarianForSequenceClassification expects eos token in input_ids
																			__lowerCAmelCase        =       np.ones((1, 1)							) * model.config.eos_token_id
																			__lowerCAmelCase        =       model(snake_case_							)
																			self.assertIsNotNone(snake_case_							)
@require_flax
@require_sentencepiece
@require_tokenizers
class      lowerCAmelCase_						(							unittest.TestCase				):



							'''simple docstring'''





							_snake_case		     =						None
							_snake_case		     =						None




							@classmethod
							def  A__		(				cls							)   ->       None:
													__lowerCAmelCase        =       f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
													return cls




							@cached_property
							def  A__		(				self							)   ->       Union[str, Any]:
													return MarianTokenizer.from_pretrained(self.model_name							)




							@property
							def  A__		(				self							)   ->       int:
													return self.tokenizer.eos_token_id




							@cached_property
							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       FlaxMarianMTModel.from_pretrained(self.model_name							)
													self.assertEqual(model.config.decoder_start_token_id  ,							model.config.pad_token_id							)
													return model




							def  A__		(				self  ,							**snake_case_							)   ->       Optional[int]:
													__lowerCAmelCase        =       self.translate_src_text(**snake_case_							)
													self.assertListEqual(self.expected_text  ,							snake_case_							)




							def  A__		(				self  ,							**snake_case_							)   ->       Any:
													__lowerCAmelCase        =       self.tokenizer(self.src_text  ,							padding=snake_case_  ,							return_tensors="""np"""  ,							**snake_case_							)
													__lowerCAmelCase        =       self.model.generate(
													    model_inputs.input_ids  ,							attention_mask=model_inputs.attention_mask  ,							num_beams=2  ,							max_length=128  ,							).sequences
													__lowerCAmelCase        =       self.tokenizer.batch_decode(snake_case_  ,							skip_special_tokens=snake_case_							)
													return generated_words
@require_flax
@require_sentencepiece
@require_tokenizers
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						'''en'''
							_snake_case		     =						'''fr'''
							_snake_case		     =						[
							    '''I am a small frog.''',
							    '''Now I can forget the 100 words of german that I know.''',
							]
							_snake_case		     =						[
							    '''Je suis une petite grenouille.''',
							    '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
							]




							@slow
							def  A__		(				self							)   ->       int:
													self._assert_generated_batch_equal_expected()
@require_flax
@require_sentencepiece
@require_tokenizers
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						'''fr'''
							_snake_case		     =						'''en'''
							_snake_case		     =						[
							    '''Donnez moi le micro.''',
							    '''Tom et Mary étaient assis à une table.''',  # Accents
							]
							_snake_case		     =						[
							    '''Give me the microphone.''',
							    '''Tom and Mary were sitting at a table.''',
							]




							@slow
							def  A__		(				self							)   ->       List[Any]:
													self._assert_generated_batch_equal_expected()
@require_flax
@require_sentencepiece
@require_tokenizers
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						'''mt'''
							_snake_case		     =						'''en'''
							_snake_case		     =						['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
							_snake_case		     =						['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']




							@slow
							def  A__		(				self							)   ->       Optional[Any]:
													self._assert_generated_batch_equal_expected()
@require_flax
@require_sentencepiece
@require_tokenizers
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						'''en'''
							_snake_case		     =						'''de'''
							_snake_case		     =						[
							    '''I am a small frog.''',
							    '''Now I can forget the 100 words of german that I know.''',
							    '''Tom asked his teacher for advice.''',
							    '''That\'s how I would do it.''',
							    '''Tom really admired Mary\'s courage.''',
							    '''Turn around and close your eyes.''',
							]
							_snake_case		     =						[
							    '''Ich bin ein kleiner Frosch.''',
							    '''Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.''',
							    '''Tom bat seinen Lehrer um Rat.''',
							    '''So würde ich das machen.''',
							    '''Tom bewunderte Marias Mut wirklich.''',
							    '''Drehen Sie sich um und schließen Sie die Augen.''',
							]




							@slow
							def  A__		(				self							)   ->       List[str]:
													self._assert_generated_batch_equal_expected()
@require_flax
@require_sentencepiece
@require_tokenizers
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						'''en'''
							_snake_case		     =						'''zh'''
							_snake_case		     =						['''My name is Wolfgang and I live in Berlin''']
							_snake_case		     =						['''我叫沃尔夫冈 我住在柏林''']




							@slow
							def  A__		(				self							)   ->       Tuple:
													self._assert_generated_batch_equal_expected()
@require_flax
@require_sentencepiece
@require_tokenizers
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						'''ru'''
							_snake_case		     =						'''fr'''
							_snake_case		     =						['''Он показал мне рукопись своей новой пьесы.''']
							_snake_case		     =						['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']




							@slow
							def  A__		(				self							)   ->       Optional[int]:
													self._assert_generated_batch_equal_expected()
@require_flax
@require_sentencepiece
@require_tokenizers
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						'''en'''
							_snake_case		     =						'''ROMANCE'''
							_snake_case		     =						[
							    '''>>fr<< Don\'t spend so much time watching TV.''',
							    '''>>pt<< Your message has been sent.''',
							    '''>>es<< He\'s two years older than me.''',
							]
							_snake_case		     =						[
							    '''Ne passez pas autant de temps à regarder la télé.''',
							    '''A sua mensagem foi enviada.''',
							    '''Es dos años más viejo que yo.''',
							]




							@slow
							def  A__		(				self							)   ->       Union[str, Any]:
													self._assert_generated_batch_equal_expected()



"""simple docstring"""
from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_			      =					TypeVar('''T''')
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							Generic[T]				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_ = 10							)   ->       Tuple:
													__lowerCAmelCase        =       []
													__lowerCAmelCase        =       limit




							def __bool__(				self							)   ->       bool:
													return bool(self.stack							)




							def __str__(				self							)   ->       str:
													return str(self.stack							)




							def  A__		(				self  ,							snake_case_							)   ->       None:
													if len(self.stack							) >= self.limit:
																			raise StackOverflowError
													self.stack.append(snake_case_							)




							def  A__		(				self							)   ->       T:
													if not self.stack:
																			raise StackUnderflowError
													return self.stack.pop()




							def  A__		(				self							)   ->       T:
													if not self.stack:
																			raise StackUnderflowError
													return self.stack[-1]




							def  A__		(				self							)   ->       bool:
													return not bool(self.stack							)




							def  A__		(				self							)   ->       bool:
													return self.size() == self.limit




							def  A__		(				self							)   ->       int:
													return len(self.stack							)




							def __contains__(				self  ,							snake_case_							)   ->       bool:
													return item in self.stack

def 				lowercase      ():
						__lowerCAmelCase        =       Stack(10		)
						assert bool(_lowerCAmelCase		) is False
						assert stack.is_empty() is True
						assert stack.is_full() is False
						assert str(_lowerCAmelCase		) == "[]"

						try:
												__lowerCAmelCase        =       stack.pop()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						try:
												__lowerCAmelCase        =       stack.peek()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						for i in range(10		):
												assert stack.size() == i
												stack.push(_lowerCAmelCase		)

						assert bool(_lowerCAmelCase		)
						assert not stack.is_empty()
						assert stack.is_full()
						assert str(_lowerCAmelCase		) == str(list(range(10		)		)		)
						assert stack.pop() == 9
						assert stack.peek() == 8

						stack.push(100		)
						assert str(_lowerCAmelCase		) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]		)

						try:
												stack.push(200		)
												raise AssertionError  # This should not happen
						except StackOverflowError:
												assert True  # This should happen

						assert not stack.is_empty()
						assert stack.size() == 10

						assert 5 in stack
						assert 55 not in stack


if __name__ == "__main__":
			test_stack()
