






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     lowercase :
		__lowercase	:	int     					=    MBartConfig
		__lowercase	:	str     					=    {}
		__lowercase	:	str     					=    '''gelu'''



		def __init__(			self    ,				A_    ,				A_=13    ,				A_=7    ,				A_=True    ,				A_=False    ,				A_=99    ,				A_=32    ,				A_=2    ,				A_=4    ,				A_=37    ,				A_=0.1    ,				A_=0.1    ,				A_=20    ,				A_=2    ,				A_=1    ,				A_=0    ,				)  -> Union[str, Any]:
				"""simple docstring"""
				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 __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				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(_a    ,				_a    ,				_a	)
				return config, inputs_dict



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					TFMBartModel(config=_a	).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(_a    ,				attention_mask=_a    ,				head_mask=_a    ,				use_cache=_a	)

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






def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase=None   ,	lowercase=None   ,	lowercase=None   ,	lowercase=None   ,	lowercase=None   ,	)       ->  Optional[Any]:
		'''simple docstring'''

		if attention_mask is None:
				UpperCamelCase          =					tf.cast(tf.math.not_equal(UpperCamelCase__   ,	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     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	List[str]     					=    (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
		__lowercase	:	List[str]     					=    (TFMBartForConditionalGeneration,) if is_tf_available() else ()
		__lowercase	:	List[Any]     					=    (
		    {
		        '''conversational''': TFMBartForConditionalGeneration,
		        '''feature-extraction''': TFMBartModel,
		        '''summarization''': TFMBartForConditionalGeneration,
		        '''text2text-generation''': TFMBartForConditionalGeneration,
		        '''translation''': TFMBartForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		__lowercase	:	List[Any]     					=    True
		__lowercase	:	Any     					=    False
		__lowercase	:	List[Any]     					=    False



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
						# Exception encountered when calling layer '...'
						return True

				return False



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					TFMBartModelTester(self	)
				UpperCamelCase          =					ConfigTester(self    ,				config_class=_a	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*_a	)






@require_sentencepiece
@require_tokenizers
@require_tf
class     lowercase (		unittest.TestCase       ):
		__lowercase	:	Union[str, Any]     					=    [
		    ''' UN Chief Says There Is No Military Solution in Syria''',
		]
		__lowercase	:	Dict     					=    [
		    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
		]
		__lowercase	:	Dict     					=    '''facebook/mbart-large-en-ro'''



		@cached_property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				return AutoTokenizer.from_pretrained(self.model_name	)



		@cached_property
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
				return model



		def __UpperCamelCase			(			self    ,				**A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.translate_src_text(**_a	)
				self.assertListEqual(self.expected_text    ,				_a	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					self.tokenizer(self.src_text    ,				**_a    ,				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(_a    ,				skip_special_tokens=_a	)
				return generated_words



		@slow
		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				self._assert_generated_batch_equal_expected()


import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Any     					=    CTRLTokenizer
		__lowercase	:	Any     					=    False
		__lowercase	:	Union[str, Any]     					=    False



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				UpperCamelCase          =					['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
				UpperCamelCase          =					dict(zip(A_    ,				range(len(A_	)	)	)	)
				UpperCamelCase          =					['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
				UpperCamelCase          =					{'unk_token': '<unk>'}

				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file']	)
				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['merges_file']	)
				with open(self.vocab_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write(json.dumps(A_	) + '\n'	)
				with open(self.merges_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write('\n'.join(A_	)	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> List[Any]:
				"""simple docstring"""
				kwargs.update(self.special_tokens_map	)
				return CTRLTokenizer.from_pretrained(self.tmpdirname    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					'adapt react readapt apt'
				UpperCamelCase          =					'adapt react readapt apt'
				return input_text, output_text



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					CTRLTokenizer(self.vocab_file    ,				self.merges_file    ,				**self.special_tokens_map	)
				UpperCamelCase          =					'adapt react readapt apt'
				UpperCamelCase          =					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
				UpperCamelCase          =					tokenizer.tokenize(A_	)
				self.assertListEqual(A_    ,				A_	)

				UpperCamelCase          =					tokens + [tokenizer.unk_token]

				UpperCamelCase          =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(A_	)    ,				A_	)
