
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
	UpperCAmelCase     				=			'''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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=None			,							__SCREAMING_SNAKE_CASE=None			,							__SCREAMING_SNAKE_CASE=None			,							__SCREAMING_SNAKE_CASE=None			,							__SCREAMING_SNAKE_CASE=None			,							__SCREAMING_SNAKE_CASE=None			,							):
	if attention_mask is None:
		lowercase					  =   np.where(input_ids != config.pad_token_id			,							1			,							0							)
	if decoder_attention_mask is None:
		lowercase					  =   np.where(decoder_input_ids != config.pad_token_id			,							1			,							0							)
	if head_mask is None:
		lowercase					  =   np.ones((config.encoder_layers, config.encoder_attention_heads)							)
	if decoder_head_mask is None:
		lowercase					  =   np.ones((config.decoder_layers, config.decoder_attention_heads)							)
	if cross_attn_head_mask is None:
		lowercase					  =   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 	A_       :




	'''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		,   ):
		lowercase					  =   parent
		lowercase					  =   batch_size
		lowercase					  =   seq_length
		lowercase					  =   is_training
		lowercase					  =   use_labels
		lowercase					  =   vocab_size
		lowercase					  =   hidden_size
		lowercase					  =   num_hidden_layers
		lowercase					  =   num_attention_heads
		lowercase					  =   intermediate_size
		lowercase					  =   hidden_act
		lowercase					  =   hidden_dropout_prob
		lowercase					  =   attention_probs_dropout_prob
		lowercase					  =   max_position_embeddings
		lowercase					  =   eos_token_id
		lowercase					  =   pad_token_id
		lowercase					  =   bos_token_id
		lowercase					  =   initializer_range





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

		lowercase					  =   shift_tokens_right(snake_case		,   1		,   2	)

		lowercase					  =   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		,   )
		lowercase					  =   prepare_marian_inputs_dict(snake_case		,   snake_case		,   snake_case	)
		return config, inputs_dict





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase  , lowercase					  =   self.prepare_config_and_inputs()
		return config, inputs_dict





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   20
		lowercase					  =   model_class_name(snake_case	)

		lowercase					  =   model.encode(inputs_dict['input_ids']	)

		lowercase  , lowercase					  =   (
		    inputs_dict['decoder_input_ids'],
		    inputs_dict['decoder_attention_mask'],
		)

		lowercase					  =   model.init_cache(decoder_input_ids.shape[0]		,   snake_case		,   snake_case	)
		lowercase					  =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,   dtype='i4'	)

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

		lowercase					  =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype='i4'	)
		lowercase					  =   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		,   )

		lowercase					  =   model.decode(snake_case		,   snake_case	)

		lowercase					  =   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		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   20
		lowercase					  =   model_class_name(snake_case	)

		lowercase					  =   model.encode(inputs_dict['input_ids']	)

		lowercase  , lowercase					  =   (
		    inputs_dict['decoder_input_ids'],
		    inputs_dict['decoder_attention_mask'],
		)

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

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

		lowercase					  =   model.decode(
		    decoder_input_ids[:, :-1]		,   snake_case		,   decoder_attention_mask=snake_case		,   past_key_values=snake_case		,   decoder_position_ids=snake_case		,   )
		lowercase					  =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype='i4'	)
		lowercase					  =   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		,   )

		lowercase					  =   model.decode(snake_case		,   snake_case		,   decoder_attention_mask=snake_case	)

		lowercase					  =   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_       (      __lowerCamelCase				,     unittest.TestCase				,     __lowerCamelCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Any										=	True
	_UpperCamelCase					:					Dict										=	(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
	_UpperCamelCase					:					Any										=	(FlaxMarianMTModel,) if is_flax_available() else ()





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   FlaxMarianModelTester(self	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase  , lowercase					  =   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      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase  , lowercase					  =   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      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase  , lowercase					  =   self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__	):
				lowercase					  =   self._prepare_for_class(snake_case		,   snake_case	)
				lowercase					  =   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'	):
					lowercase					  =   encode_jitted(**snake_case	).to_tuple()

				with self.subTest('JIT Disabled'	):
					with jax.disable_jit():
						lowercase					  =   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      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase  , lowercase					  =   self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__	):
				lowercase					  =   model_class(snake_case	)
				lowercase					  =   model.encode(inputs_dict['input_ids']		,   inputs_dict['attention_mask']	)

				lowercase					  =   {
				    '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'	):
					lowercase					  =   decode_jitted(**snake_case	).to_tuple()

				with self.subTest('JIT Disabled'	):
					with jax.disable_jit():
						lowercase					  =   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      SCREAMING_SNAKE_CASE__						(	self	):
		for model_class_name in self.all_model_classes:
			lowercase					  =   model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'	)
			# FlaxMarianForSequenceClassification expects eos token in input_ids
			lowercase					  =   np.ones((1, 1)	) * model.config.eos_token_id
			lowercase					  =   model(snake_case	)
			self.assertIsNotNone(snake_case	)







@require_flax
@require_sentencepiece
@require_tokenizers
class 	A_       (      unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Optional[int]										=	None
	_UpperCamelCase					:					List[str]										=	None





	@classmethod
	def      SCREAMING_SNAKE_CASE__						(	cls	):
		lowercase					  =   F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
		return cls





	@cached_property
	def      SCREAMING_SNAKE_CASE__						(	self	):
		return MarianTokenizer.from_pretrained(self.model_name	)





	@property
	def      SCREAMING_SNAKE_CASE__						(	self	):
		return self.tokenizer.eos_token_id





	@cached_property
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   FlaxMarianMTModel.from_pretrained(self.model_name	)
		self.assertEqual(model.config.decoder_start_token_id		,   model.config.pad_token_id	)
		return model





	def      SCREAMING_SNAKE_CASE__						(	self		,   **snake_case	):
		lowercase					  =   self.translate_src_text(**snake_case	)
		self.assertListEqual(self.expected_text		,   snake_case	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   **snake_case	):
		lowercase					  =   self.tokenizer(self.src_text		,   padding=snake_case		,   return_tensors='np'		,   **snake_case	)
		lowercase					  =   self.model.generate(
		    model_inputs.input_ids		,   attention_mask=model_inputs.attention_mask		,   num_beams=2		,   max_length=128		,   ).sequences
		lowercase					  =   self.tokenizer.batch_decode(snake_case		,   skip_special_tokens=snake_case	)
		return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Optional[int]										=	"""en"""
	_UpperCamelCase					:					Optional[int]										=	"""fr"""
	_UpperCamelCase					:					Dict										=	[
	    """I am a small frog.""",
	    """Now I can forget the 100 words of german that I know.""",
	]
	_UpperCamelCase					:					List[Any]										=	[
	    """Je suis une petite grenouille.""",
	    """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
	]





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Optional[Any]										=	"""fr"""
	_UpperCamelCase					:					Dict										=	"""en"""
	_UpperCamelCase					:					Union[str, Any]										=	[
	    """Donnez moi le micro.""",
	    """Tom et Mary étaient assis à une table.""",  # Accents
	]
	_UpperCamelCase					:					Tuple										=	[
	    """Give me the microphone.""",
	    """Tom and Mary were sitting at a table.""",
	]





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


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





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					List[Any]										=	"""en"""
	_UpperCamelCase					:					Any										=	"""de"""
	_UpperCamelCase					:					Dict										=	[
	    """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.""",
	]
	_UpperCamelCase					:					Dict										=	[
	    """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      SCREAMING_SNAKE_CASE__						(	self	):
		self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					List[str]										=	"""en"""
	_UpperCamelCase					:					str										=	"""zh"""
	_UpperCamelCase					:					List[Any]										=	["""My name is Wolfgang and I live in Berlin"""]
	_UpperCamelCase					:					str										=	["""我叫沃尔夫冈 我住在柏林"""]





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					str										=	"""ru"""
	_UpperCamelCase					:					Union[str, Any]										=	"""fr"""
	_UpperCamelCase					:					Any										=	["""Он показал мне рукопись своей новой пьесы."""]
	_UpperCamelCase					:					Tuple										=	["""Il m'a montré le manuscrit de sa nouvelle pièce."""]





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


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





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		self._assert_generated_batch_equal_expected()



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase     				=			{
    '''configuration_dpr''': ['''DPR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPRConfig'''],
    '''tokenization_dpr''': [
        '''DPRContextEncoderTokenizer''',
        '''DPRQuestionEncoderTokenizer''',
        '''DPRReaderOutput''',
        '''DPRReaderTokenizer''',
    ],
}


try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			[
	    '''DPRContextEncoderTokenizerFast''',
	    '''DPRQuestionEncoderTokenizerFast''',
	    '''DPRReaderTokenizerFast''',
	]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			[
	    '''DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DPRContextEncoder''',
	    '''DPRPretrainedContextEncoder''',
	    '''DPRPreTrainedModel''',
	    '''DPRPretrainedQuestionEncoder''',
	    '''DPRPretrainedReader''',
	    '''DPRQuestionEncoder''',
	    '''DPRReader''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			[
	    '''TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFDPRContextEncoder''',
	    '''TFDPRPretrainedContextEncoder''',
	    '''TFDPRPretrainedQuestionEncoder''',
	    '''TFDPRPretrainedReader''',
	    '''TFDPRQuestionEncoder''',
	    '''TFDPRReader''',
	]


if TYPE_CHECKING:
	from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
	from .tokenization_dpr import (
	    DPRContextEncoderTokenizer,
	    DPRQuestionEncoderTokenizer,
	    DPRReaderOutput,
	    DPRReaderTokenizer,
	)

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_dpr_fast import (
		    DPRContextEncoderTokenizerFast,
		    DPRQuestionEncoderTokenizerFast,
		    DPRReaderTokenizerFast,
		)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dpr import (
		    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DPRContextEncoder,
		    DPRPretrainedContextEncoder,
		    DPRPreTrainedModel,
		    DPRPretrainedQuestionEncoder,
		    DPRPretrainedReader,
		    DPRQuestionEncoder,
		    DPRReader,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_dpr import (
		    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDPRContextEncoder,
		    TFDPRPretrainedContextEncoder,
		    TFDPRPretrainedQuestionEncoder,
		    TFDPRPretrainedReader,
		    TFDPRQuestionEncoder,
		    TFDPRReader,
		)

else:
	import sys

	UpperCAmelCase     				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

