
from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel







@require_tf
class 			UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase											=					MarianConfig
	_lowerCAmelCase											=					{}
	_lowerCAmelCase											=					"gelu"





	def __init__(      self					,							UpperCamelCase__					,							UpperCamelCase__=13					,							UpperCamelCase__=7					,							UpperCamelCase__=True					,							UpperCamelCase__=False					,							UpperCamelCase__=99					,							UpperCamelCase__=32					,							UpperCamelCase__=2					,							UpperCamelCase__=4					,							UpperCamelCase__=37					,							UpperCamelCase__=0.1					,							UpperCamelCase__=0.1					,							UpperCamelCase__=20					,							UpperCamelCase__=2					,							UpperCamelCase__=1					,							UpperCamelCase__=0					,							):
					A__	:						Tuple								=				parent
					A__	:						List[str]								=				batch_size
					A__	:						Optional[int]								=				seq_length
					A__	:						Optional[Any]								=				is_training
					A__	:						int								=				use_labels
					A__	:						Dict								=				vocab_size
					A__	:						Tuple								=				hidden_size
					A__	:						List[str]								=				num_hidden_layers
					A__	:						List[str]								=				num_attention_heads
					A__	:						str								=				intermediate_size

					A__	:						List[str]								=				hidden_dropout_prob
					A__	:						Optional[int]								=				attention_probs_dropout_prob
					A__	:						Any								=				max_position_embeddings
					A__	:						str								=				eos_token_id
					A__	:						str								=				pad_token_id
					A__	:						int								=				bos_token_id





	def 	__snake_case					(      self		):
					A__	:						Optional[Any]								=				ids_tensor([self.batch_size, self.seq_length - 1]					,							self.vocab_size		)
					A__	:						List[Any]								=				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)					,							1		)
					A__	:						Optional[int]								=				tf.concat([input_ids, eos_tensor]					,							axis=1		)

					A__	:						List[Any]								=				ids_tensor([self.batch_size, self.seq_length]					,							self.vocab_size		)

					A__	:						List[str]								=				self.config_cls(
					    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_ids=[2]					,							bos_token_id=self.bos_token_id					,							pad_token_id=self.pad_token_id					,							decoder_start_token_id=self.pad_token_id					,							**self.config_updates					,							)
					A__	:						Optional[int]								=				prepare_marian_inputs_dict(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		)
					return config, inputs_dict





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__		):
					A__	:						Any								=				TFMarianModel(config=UpperCamelCase__		).get_decoder()
					A__	:						Tuple								=				inputs_dict['''input_ids''']

					A__	:						List[Any]								=				input_ids[:1, :]
					A__	:						Union[str, Any]								=				inputs_dict['''attention_mask'''][:1, :]
					A__	:						int								=				inputs_dict['''head_mask''']
					A__	:						Dict								=				1

					# first forward pass
					A__	:						Optional[Any]								=				model(UpperCamelCase__					,							attention_mask=UpperCamelCase__					,							head_mask=UpperCamelCase__					,							use_cache=UpperCamelCase__		)

					A__							,				A__	:						int								=				outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					A__	:						Optional[Any]								=				ids_tensor((self.batch_size, 3)					,							config.vocab_size		)
					A__	:						Tuple								=				tf.cast(ids_tensor((self.batch_size, 3)					,							2		)					,							tf.inta		)

					# append to next input_ids and
					A__	:						Union[str, Any]								=				tf.concat([input_ids, next_tokens]					,							axis=-1		)
					A__	:						Optional[Any]								=				tf.concat([attention_mask, next_attn_mask]					,							axis=-1		)

					A__	:						Union[str, Any]								=				model(UpperCamelCase__					,							attention_mask=UpperCamelCase__		)[0]
					A__	:						List[Any]								=				model(UpperCamelCase__					,							attention_mask=UpperCamelCase__					,							past_key_values=UpperCamelCase__		)[0]

					self.parent.assertEqual(next_tokens.shape[1]					,							output_from_past.shape[1]		)

					# select random slice
					A__	:						Optional[Any]								=				int(ids_tensor((1,)					,							output_from_past.shape[-1]		)		)
					A__	:						int								=				output_from_no_past[:, -3:, random_slice_idx]
					A__	:						str								=				output_from_past[:, :, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(UpperCamelCase__					,							UpperCamelCase__					,							rtol=1e-3		)




def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Tuple				,    __UpperCamelCase			:    int				,    __UpperCamelCase			:    List[str]				,    __UpperCamelCase			:    Optional[int]=None				,    __UpperCamelCase			:    Dict=None				,    __UpperCamelCase			:    List[Any]=None				,    __UpperCamelCase			:    str=None				,    __UpperCamelCase			:    Optional[Any]=None				,    )			->							Union[str, Any]:

				"""simple docstring"""







				if attention_mask is None:
								A__	:						int								=				tf.cast(tf.math.not_equal(__UpperCamelCase				,    config.pad_token_id							)				,    tf.inta							)
				if decoder_attention_mask is None:
								A__	:						Optional[int]								=				tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape				,    dtype=tf.inta							),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]				,    config.pad_token_id							)				,    tf.inta							),
								    ]				,    axis=-1				,    )
				if head_mask is None:
								A__	:						Optional[Any]								=				tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
				if decoder_head_mask is None:
								A__	:						Dict								=				tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
				if cross_attn_head_mask is None:
								A__	:						Union[str, Any]								=				tf.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": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}







@require_tf
class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      unittest.TestCase ):







	'''simple docstring'''
	_lowerCAmelCase											=					(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
	_lowerCAmelCase											=					(TFMarianMTModel,) if is_tf_available() else ()
	_lowerCAmelCase											=					(
	    {
	        "conversational": TFMarianMTModel,
	        "feature-extraction": TFMarianModel,
	        "summarization": TFMarianMTModel,
	        "text2text-generation": TFMarianMTModel,
	        "translation": TFMarianMTModel,
	    }
	    if is_tf_available()
	    else {}
	)
	_lowerCAmelCase											=					True
	_lowerCAmelCase											=					False
	_lowerCAmelCase											=					False





	def 	__snake_case					(      self		):
					A__	:						Dict								=				TFMarianModelTester(self		)
					A__	:						Tuple								=				ConfigTester(self					,							config_class=UpperCamelCase__		)





	def 	__snake_case					(      self		):
					self.config_tester.run_common_tests()





	def 	__snake_case					(      self		):
					A__	:						Tuple								=				self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__		)







@require_tf
class 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''
	_lowerCAmelCase											=					1_000  # show more chars for failing integration tests





	@classmethod
	def 	__snake_case					(      cls		):
					A__	:						Dict								=				F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
					return cls





	@cached_property
	def 	__snake_case					(      self		):
					return AutoTokenizer.from_pretrained(self.model_name		)





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





	@cached_property
	def 	__snake_case					(      self		):
					warnings.simplefilter('''error'''		)
					A__	:						TFMarianMTModel								=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
					assert isinstance(UpperCamelCase__					,							UpperCamelCase__		)
					A__	:						int								=				model.config
					self.assertListEqual(c.bad_words_ids					,							[[c.pad_token_id]]		)
					self.assertEqual(c.max_length					,							512		)
					self.assertEqual(c.decoder_start_token_id					,							c.pad_token_id		)
					return model





	def 	__snake_case					(      self					,							**UpperCamelCase__		):
					A__	:						Tuple								=				self.translate_src_text(**UpperCamelCase__		)
					self.assertListEqual(self.expected_text					,							UpperCamelCase__		)





	def 	__snake_case					(      self					,							**UpperCamelCase__		):
					A__	:						Union[str, Any]								=				self.tokenizer(self.src_text					,							**UpperCamelCase__					,							padding=UpperCamelCase__					,							return_tensors='''tf'''		)
					A__	:						List[str]								=				self.model.generate(
					    model_inputs.input_ids					,							attention_mask=model_inputs.attention_mask					,							num_beams=2					,							max_length=128		)
					A__	:						List[Any]								=				self.tokenizer.batch_decode(generated_ids.numpy()					,							skip_special_tokens=UpperCamelCase__		)
					return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_ ):







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





	@unittest.skip('''Skipping until #12647 is resolved.'''		)
	@slow
	def 	__snake_case					(      self		):
					self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_ ):







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





	@unittest.skip('''Skipping until #12647 is resolved.'''		)
	@slow
	def 	__snake_case					(      self		):
					self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_ ):







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





	@unittest.skip('''Skipping until #12647 is resolved.'''		)
	@slow
	def 	__snake_case					(      self		):
					self._assert_generated_batch_equal_expected()





	@unittest.skip('''Skipping until #12647 is resolved.'''		)
	@slow
	def 	__snake_case					(      self		):
					A__	:						Optional[int]								=				TranslationPipeline(self.model					,							self.tokenizer					,							framework='''tf'''		)
					A__	:						Optional[int]								=				pipeline(self.src_text		)
					self.assertEqual(self.expected_text					,							[x['''translation_text'''] for x in output]		)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Tuple							 =      [
					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaubertForMultipleChoice',
					    'FlaubertForQuestionAnswering',
					    'FlaubertForQuestionAnsweringSimple',
					    'FlaubertForSequenceClassification',
					    'FlaubertForTokenClassification',
					    'FlaubertModel',
					    'FlaubertWithLMHeadModel',
					    'FlaubertPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Dict							 =      [
					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFFlaubertForMultipleChoice',
					    'TFFlaubertForQuestionAnsweringSimple',
					    'TFFlaubertForSequenceClassification',
					    'TFFlaubertForTokenClassification',
					    'TFFlaubertModel',
					    'TFFlaubertPreTrainedModel',
					    'TFFlaubertWithLMHeadModel',
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					_SCREAMING_SNAKE_CASE     :   List[Any]							 =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)