


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   _a      :
    a_    :				Union[str, Any]             =    MBartConfig
    a_    :				Dict             =    {}
    a_    :				List[str]             =    'gelu'
    def __init__( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]=13							,					SCREAMING_SNAKE_CASE__							:					List[str]=7							,					SCREAMING_SNAKE_CASE__							:					str=True							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=False							,					SCREAMING_SNAKE_CASE__							:					Any=99							,					SCREAMING_SNAKE_CASE__							:					int=32							,					SCREAMING_SNAKE_CASE__							:					List[str]=2							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=4							,					SCREAMING_SNAKE_CASE__							:					Any=37							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0.1							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0.1							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=20							,					SCREAMING_SNAKE_CASE__							:					Any=2							,					SCREAMING_SNAKE_CASE__							:					List[str]=1							,					SCREAMING_SNAKE_CASE__							:					int=0							,					):
          lowerCamelCase__           =		parent
          lowerCamelCase__           =		batch_size
          lowerCamelCase__           =		seq_length
          lowerCamelCase__           =		is_training
          lowerCamelCase__           =		use_labels
          lowerCamelCase__           =		vocab_size
          lowerCamelCase__           =		hidden_size
          lowerCamelCase__           =		num_hidden_layers
          lowerCamelCase__           =		num_attention_heads
          lowerCamelCase__           =		intermediate_size
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		max_position_embeddings
          lowerCamelCase__           =		eos_token_id
          lowerCamelCase__           =		pad_token_id
          lowerCamelCase__           =		bos_token_id
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		ids_tensor([self.batch_size, self.seq_length - 1]							,					self.vocab_size				)
          lowerCamelCase__           =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)							,					1				)
          lowerCamelCase__           =		tf.concat([input_ids, eos_tensor]							,					axis=1				)

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

          lowerCamelCase__           =		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							,					)
          lowerCamelCase__           =		prepare_mbart_inputs_dict(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
          return config, inputs_dict


    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					str				):
          lowerCamelCase__           =		TFMBartModel(config=SCREAMING_SNAKE_CASE__				).get_decoder()
          lowerCamelCase__           =		inputs_dict['input_ids']

          lowerCamelCase__           =		input_ids[:1, :]
          lowerCamelCase__           =		inputs_dict['attention_mask'][:1, :]
          lowerCamelCase__           =		inputs_dict['head_mask']
          lowerCamelCase__           =		1

          # first forward pass
          lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__							,					attention_mask=SCREAMING_SNAKE_CASE__							,					head_mask=SCREAMING_SNAKE_CASE__							,					use_cache=SCREAMING_SNAKE_CASE__				)

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





def   snake_case						(				_a:     int		,   _a:     Tuple		,   _a:     Union[str, Any]		,   _a:     Union[str, Any]=None		,   _a:     Optional[int]=None		,   _a:     Optional[int]=None		,   _a:     Optional[Any]=None		,   _a:     List[str]=None		,   )->		Union[str, Any]:
      '''simple docstring'''


      if attention_mask is None:
            lowerCamelCase__           =		tf.cast(tf.math.not_equal(__SCREAMING_SNAKE_CASE		,   config.pad_token_id							)		,   tf.inta							)
      if decoder_attention_mask is None:
            lowerCamelCase__           =		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:
            lowerCamelCase__           =		tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
      if decoder_head_mask is None:
            lowerCamelCase__           =		tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
      if cross_attn_head_mask is None:
            lowerCamelCase__           =		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   _a      (      lowercase__		,		lowercase__		,		unittest.TestCase ):
    a_    :				int             =    (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
    a_    :				Any             =    (TFMBartForConditionalGeneration,) if is_tf_available() else ()
    a_    :				Any             =    (
        {
            'conversational': TFMBartForConditionalGeneration,
            'feature-extraction': TFMBartModel,
            'summarization': TFMBartForConditionalGeneration,
            'text2text-generation': TFMBartForConditionalGeneration,
            'translation': TFMBartForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    a_    :				str             =    True
    a_    :				Union[str, Any]             =    False
    a_    :				Dict             =    False
    def      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					List[str]				):
          if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
                # Exception encountered when calling layer '...'
                return True

          return False
    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__           =		TFMBartModelTester(self				)
          lowerCamelCase__           =		ConfigTester(self							,					config_class=SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          self.config_tester.run_common_tests()


    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__				)





@require_sentencepiece
@require_tokenizers
@require_tf
class   _a      (      unittest.TestCase ):
    a_    :				List[Any]             =    [
        ' UN Chief Says There Is No Military Solution in Syria',
    ]
    a_    :				Optional[int]             =    [
        'Şeful ONU declară că nu există o soluţie militară în Siria',
    ]
    a_    :				int             =    'facebook/mbart-large-en-ro'
    @cached_property
    def      _UpperCamelCase       ( self							:					List[Any]				):
          return AutoTokenizer.from_pretrained(self.model_name				)
    @cached_property
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
          return model
    def      _UpperCamelCase       ( self							:					List[Any]							,					**SCREAMING_SNAKE_CASE__							:					Dict				):
          lowerCamelCase__           =		self.translate_src_text(**SCREAMING_SNAKE_CASE__				)
          self.assertListEqual(self.expected_text							,					SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Any							,					**SCREAMING_SNAKE_CASE__							:					Optional[int]				):
          lowerCamelCase__           =		self.tokenizer(self.src_text							,					**SCREAMING_SNAKE_CASE__							,					return_tensors='tf'				)
          lowerCamelCase__           =		self.model.generate(
              model_inputs.input_ids							,					attention_mask=model_inputs.attention_mask							,					num_beams=2				)
          lowerCamelCase__           =		self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							,					skip_special_tokens=SCREAMING_SNAKE_CASE__				)
          return generated_words


    @slow
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          self._assert_generated_batch_equal_expected()







"""simple docstring"""


from typing import TYPE_CHECKING

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


_snake_case          =							{
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
       "M2M100ForConditionalGeneration",
       "M2M100Model",
       "M2M100PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
   from .tokenization_mam_aaa import MaMaaaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mam_aaa import (
          M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaMaaaForConditionalGeneration,
          MaMaaaModel,
          MaMaaaPreTrainedModel,
      )


else:
   import sys

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



