
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, 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, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class        _UpperCamelCase				:






			'''simple docstring'''

			__UpperCamelCase      :						str					      = MBartConfig
			__UpperCamelCase      :						List[str]					      = {}
			__UpperCamelCase      :						List[Any]					      = """gelu"""

			def __init__(       self :       List[str]						,	snake_case_ :       Dict						,	snake_case_ :       int=13						,	snake_case_ :       str=7						,	snake_case_ :       List[str]=True						,	snake_case_ :       Any=False						,	snake_case_ :       Any=99						,	snake_case_ :       Tuple=32						,	snake_case_ :       Dict=2						,	snake_case_ :       Optional[Any]=4						,	snake_case_ :       Any=37						,	snake_case_ :       Union[str, Any]=0.1						,	snake_case_ :       Union[str, Any]=0.1						,	snake_case_ :       Tuple=20						,	snake_case_ :       Optional[int]=2						,	snake_case_ :       Union[str, Any]=1						,	snake_case_ :       Optional[int]=0						,	):
							UpperCamelCase_:							Optional[int]	=						parent
							UpperCamelCase_:							Union[str, Any]	=						batch_size
							UpperCamelCase_:							List[str]	=						seq_length
							UpperCamelCase_:							Dict	=						is_training
							UpperCamelCase_:							Optional[int]	=						use_labels
							UpperCamelCase_:							Optional[Any]	=						vocab_size
							UpperCamelCase_:							Dict	=						hidden_size
							UpperCamelCase_:							int	=						num_hidden_layers
							UpperCamelCase_:							Optional[Any]	=						num_attention_heads
							UpperCamelCase_:							str	=						intermediate_size
							UpperCamelCase_:							Optional[int]	=						hidden_dropout_prob
							UpperCamelCase_:							Dict	=						attention_probs_dropout_prob
							UpperCamelCase_:							Any	=						max_position_embeddings
							UpperCamelCase_:							Union[str, Any]	=						eos_token_id
							UpperCamelCase_:							Optional[int]	=						pad_token_id
							UpperCamelCase_:							Optional[Any]	=						bos_token_id

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

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

							UpperCamelCase_:							Tuple	=						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						,	)
							UpperCamelCase_:							Any	=						prepare_mbart_inputs_dict(snake_case_						,	snake_case_						,	snake_case_      )
							return config, inputs_dict




			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	snake_case_ :       Tuple						,	snake_case_ :       str      ):
							UpperCamelCase_:							List[Any]	=						TFMBartModel(config=snake_case_      ).get_decoder()
							UpperCamelCase_:							Any	=						inputs_dict["""input_ids"""]

							UpperCamelCase_:							List[str]	=						input_ids[:1, :]
							UpperCamelCase_:							Tuple	=						inputs_dict["""attention_mask"""][:1, :]
							UpperCamelCase_:							Union[str, Any]	=						inputs_dict["""head_mask"""]
							UpperCamelCase_:							Any	=						1

							# first forward pass
							UpperCamelCase_:							List[str]	=						model(snake_case_						,	attention_mask=snake_case_						,	head_mask=snake_case_						,	use_cache=snake_case_      )

							UpperCamelCase_,   UpperCamelCase_:							List[str]	=						outputs.to_tuple()
							UpperCamelCase_:							Union[str, Any]	=						past_key_values[1]







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=None     ,    lowerCamelCase=None     ,    lowerCamelCase=None     ,    lowerCamelCase=None     ,    lowerCamelCase=None     ,    )  ->	Any:
				if attention_mask is None:
								UpperCamelCase_:							Tuple	=						tf.cast(tf.math.not_equal(lowerCamelCase     ,    config.pad_token_id						)     ,    tf.inta						)
				if decoder_attention_mask is None:
								UpperCamelCase_:							str	=						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:
								UpperCamelCase_:							Optional[int]	=						tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
				if decoder_head_mask is None:
								UpperCamelCase_:							Tuple	=						tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
				if cross_attn_head_mask is None:
								UpperCamelCase_:							Tuple	=						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				(					_A     ,       _A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						Union[str, Any]					      = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
			__UpperCamelCase      :						List[Any]					      = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
			__UpperCamelCase      :						Optional[Any]					      = (
			    {
			        """conversational""": TFMBartForConditionalGeneration,
			        """feature-extraction""": TFMBartModel,
			        """summarization""": TFMBartForConditionalGeneration,
			        """text2text-generation""": TFMBartForConditionalGeneration,
			        """translation""": TFMBartForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			__UpperCamelCase      :						str					      = True
			__UpperCamelCase      :						Optional[int]					      = False
			__UpperCamelCase      :						List[str]					      = False

			def       lowerCAmelCase__	(       self :       List[str]						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Dict						,	snake_case_ :       Tuple						,	snake_case_ :       str      ):
							if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
											# Exception encountered when calling layer '...'
											return True

							return False

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							List[str]	=						TFMBartModelTester(self      )
							UpperCamelCase_:							List[Any]	=						ConfigTester(self						,	config_class=snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							self.config_tester.run_common_tests()




			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Any	=						self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*snake_case_      )




@require_sentencepiece
@require_tokenizers
@require_tf
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						Tuple					      = [
			    """ UN Chief Says There Is No Military Solution in Syria""",
			]
			__UpperCamelCase      :						Any					      = [
			    """Şeful ONU declară că nu există o soluţie militară în Siria""",
			]
			__UpperCamelCase      :						Optional[int]					      = """facebook/mbart-large-en-ro"""

			@cached_property
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							return AutoTokenizer.from_pretrained(self.model_name      )

			@cached_property
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							List[str]	=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
							return model

			def       lowerCAmelCase__	(       self :       Any						,	**snake_case_ :       Union[str, Any]      ):
							UpperCamelCase_:							Optional[Any]	=						self.translate_src_text(**snake_case_      )
							self.assertListEqual(self.expected_text						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       List[str]						,	**snake_case_ :       Any      ):
							UpperCamelCase_:							str	=						self.tokenizer(self.src_text						,	**snake_case_						,	return_tensors="""tf"""      )
							UpperCamelCase_:							Any	=						self.model.generate(
							    model_inputs.input_ids						,	attention_mask=model_inputs.attention_mask						,	num_beams=2      )
							UpperCamelCase_:							Dict	=						self.tokenizer.batch_decode(snake_case_						,	skip_special_tokens=snake_case_      )
							return generated_words




			@slow
			def       lowerCAmelCase__	(       self :       str      ):
							self._assert_generated_batch_equal_expected()








import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							List[Any]	=						inspect.getfile(accelerate.test_utils      )
							UpperCamelCase_:							List[str]	=						os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["""scripts""", """test_script.py"""]      )
							UpperCamelCase_:							str	=						os.path.sep.join(inspect.getfile(self.__class__      ).split(os.path.sep      )[:-1]      )




			@require_tpu
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Any	=						f'''
            {self.test_dir}/xla_spawn.py
            --num_cores 8
            {self.test_file_path}
        '''.split()
							UpperCamelCase_:							Dict	=						[sys.executable] + distributed_args
							execute_subprocess_async(snake_case_						,	env=os.environ.copy()      )






