



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 	lowerCamelCase      :







					'''simple docstring'''







					lowerCAmelCase_				:    Union[str, Any]									=  MBartConfig
					lowerCAmelCase_				:    Dict									=  {}
					lowerCAmelCase_				:    List[str]									=  'gelu'



					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=13    ,	lowerCAmelCase=7    ,	lowerCAmelCase=True    ,	lowerCAmelCase=False    ,	lowerCAmelCase=99    ,	lowerCAmelCase=32    ,	lowerCAmelCase=2    ,	lowerCAmelCase=4    ,	lowerCAmelCase=37    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=20    ,	lowerCAmelCase=2    ,	lowerCAmelCase=1    ,	lowerCAmelCase=0    ,	):
							UpperCAmelCase_						=				parent
							UpperCAmelCase_						=				batch_size
							UpperCAmelCase_						=				seq_length
							UpperCAmelCase_						=				is_training
							UpperCAmelCase_						=				use_labels
							UpperCAmelCase_						=				vocab_size
							UpperCAmelCase_						=				hidden_size
							UpperCAmelCase_						=				num_hidden_layers
							UpperCAmelCase_						=				num_attention_heads
							UpperCAmelCase_						=				intermediate_size
							UpperCAmelCase_						=				hidden_dropout_prob
							UpperCAmelCase_						=				attention_probs_dropout_prob
							UpperCAmelCase_						=				max_position_embeddings
							UpperCAmelCase_						=				eos_token_id
							UpperCAmelCase_						=				pad_token_id
							UpperCAmelCase_						=				bos_token_id



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

							UpperCAmelCase_						=				ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size     )

							UpperCAmelCase_						=				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_						=				prepare_mbart_inputs_dict(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )
							return config, inputs_dict



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				TFMBartModel(config=lowerCAmelCase     ).get_decoder()
							UpperCAmelCase_						=				inputs_dict["input_ids"]

							UpperCAmelCase_						=				input_ids[:1, :]
							UpperCAmelCase_						=				inputs_dict["attention_mask"][:1, :]
							UpperCAmelCase_						=				inputs_dict["head_mask"]
							UpperCAmelCase_						=				1

							# first forward pass
							UpperCAmelCase_						=				model(lowerCAmelCase    ,	attention_mask=lowerCAmelCase    ,	head_mask=lowerCAmelCase    ,	use_cache=lowerCAmelCase     )

							UpperCAmelCase_					,						UpperCAmelCase_						=				outputs.to_tuple()
							UpperCAmelCase_						=				past_key_values[1]







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __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			,     )		->							Union[str, Any]:
		if attention_mask is None:
				UpperCAmelCase_						=				tf.cast(tf.math.not_equal(__SCREAMING_SNAKE_CASE			,     config.pad_token_id )			,     tf.inta )
		if decoder_attention_mask is None:
				UpperCAmelCase_						=				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_						=				tf.ones((config.encoder_layers, config.encoder_attention_heads) )
		if decoder_head_mask is None:
				UpperCAmelCase_						=				tf.ones((config.decoder_layers, config.decoder_attention_heads) )
		if cross_attn_head_mask is None:
				UpperCAmelCase_						=				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 	lowerCamelCase      (						lowercase__,     lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







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



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
									# Exception encountered when calling layer '...'
									return True

							return False



					def    A__		(			self     ):
							UpperCAmelCase_						=				TFMBartModelTester(self     )
							UpperCAmelCase_						=				ConfigTester(self    ,	config_class=lowerCAmelCase     )



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



					def    A__		(			self     ):
							UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase     )





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







					'''simple docstring'''







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



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



					@cached_property
					def    A__		(			self     ):
							UpperCAmelCase_						=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
							return model



					def    A__		(			self    ,	**lowerCAmelCase     ):
							UpperCAmelCase_						=				self.translate_src_text(**lowerCAmelCase     )
							self.assertListEqual(self.expected_text    ,	lowerCAmelCase     )



					def    A__		(			self    ,	**lowerCAmelCase     ):
							UpperCAmelCase_						=				self.tokenizer(self.src_text    ,	**lowerCAmelCase    ,	return_tensors="tf"     )
							UpperCAmelCase_						=				self.model.generate(
							    model_inputs.input_ids    ,	attention_mask=model_inputs.attention_mask    ,	num_beams=2     )
							UpperCAmelCase_						=				self.tokenizer.batch_decode(lowerCAmelCase    ,	skip_special_tokens=lowerCAmelCase     )
							return generated_words



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






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE     				=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTBigCodeForSequenceClassification",
			    "GPTBigCodeForTokenClassification",
			    "GPTBigCodeForCausalLM",
			    "GPTBigCodeModel",
			    "GPTBigCodePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_bigcode import (
						    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTBigCodeForCausalLM,
						    GPTBigCodeForSequenceClassification,
						    GPTBigCodeForTokenClassification,
						    GPTBigCodeModel,
						    GPTBigCodePreTrainedModel,
						)


else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

