

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'''







       lowercase_						=    MBartConfig
       lowercase_						=    {}
       lowercase_						=    """gelu"""

       def __init__(							self ,   lowercase__ ,   lowercase__=1_3 ,   lowercase__=7 ,   lowercase__=True ,   lowercase__=False ,   lowercase__=9_9 ,   lowercase__=3_2 ,   lowercase__=2 ,   lowercase__=4 ,   lowercase__=3_7 ,   lowercase__=0.1 ,   lowercase__=0.1 ,   lowercase__=2_0 ,   lowercase__=2 ,   lowercase__=1 ,   lowercase__=0 ,   ):







             '''simple docstring'''



             __A    =parent
             __A    =batch_size
             __A    =seq_length
             __A    =is_training
             __A    =use_labels
             __A    =vocab_size
             __A    =hidden_size
             __A    =num_hidden_layers
             __A    =num_attention_heads
             __A    =intermediate_size
             __A    =hidden_dropout_prob
             __A    =attention_probs_dropout_prob
             __A    =max_position_embeddings
             __A    =eos_token_id
             __A    =pad_token_id
             __A    =bos_token_id

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

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

             __A    =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    =prepare_mbart_inputs_dict(lowercase__ ,   lowercase__ ,   lowercase__     )
             return config, inputs_dict


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =TFMBartModel(config=lowercase__     ).get_decoder()
             __A    =inputs_dict['''input_ids''']

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

             # first forward pass
             __A    =model(lowercase__ ,   attention_mask=lowercase__ ,   head_mask=lowercase__ ,   use_cache=lowercase__     )

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





def      A__					(			__A  :						Any     ,				__A  :						Union[str, Any]     ,				__A  :						Optional[Any]     ,				__A  :						str=None     ,				__A  :						str=None     ,				__A  :						Dict=None     ,				__A  :						Optional[Any]=None     ,				__A  :						Dict=None     ,				)					->Union[str, Any]:
      if attention_mask is None:
            __A    =tf.cast(tf.math.not_equal(__A     ,				config.pad_token_id			)     ,				tf.inta			)
      if decoder_attention_mask is None:
            __A    =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    =tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
      if decoder_head_mask is None:
            __A    =tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
      if cross_attn_head_mask is None:
            __A    =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__  (					__magic_name__				,							__magic_name__				,							unittest.TestCase			):



       '''simple docstring'''







       lowercase_						=    (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
       lowercase_						=    (TFMBartForConditionalGeneration,) if is_tf_available() else ()
       lowercase_						=    (
           {
               """conversational""": TFMBartForConditionalGeneration,
               """feature-extraction""": TFMBartModel,
               """summarization""": TFMBartForConditionalGeneration,
               """text2text-generation""": TFMBartForConditionalGeneration,
               """translation""": TFMBartForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       lowercase_						=    True
       lowercase_						=    False
       lowercase_						=    False

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
                   # Exception encountered when calling layer '...'
                   return True

             return False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =TFMBartModelTester(self     )
             __A    =ConfigTester(self ,   config_class=lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             self.config_tester.run_common_tests()


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*lowercase__     )





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



       '''simple docstring'''







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

       @cached_property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return AutoTokenizer.from_pretrained(self.model_name     )

       @cached_property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
             return model

       def     __UpperCamelCase     (							self ,   **lowercase__     ):







             '''simple docstring'''



             __A    =self.translate_src_text(**lowercase__     )
             self.assertListEqual(self.expected_text ,   lowercase__     )

       def     __UpperCamelCase     (							self ,   **lowercase__     ):







             '''simple docstring'''



             __A    =self.tokenizer(self.src_text ,   **lowercase__ ,   return_tensors='''tf'''     )
             __A    =self.model.generate(
                 model_inputs.input_ids ,   attention_mask=model_inputs.attention_mask ,   num_beams=2     )
             __A    =self.tokenizer.batch_decode(lowercase__ ,   skip_special_tokens=lowercase__     )
             return generated_words


       @slow
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             self._assert_generated_batch_equal_expected()





import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def      A__					(			__A  :						List[str]			)					->str:
      __A    =[]
      for line in lines:
            __A    =re.sub(r'''#.*'''     ,				''''''     ,				__A			)  # remove comments
            if line:
                  filtered_lines.append(__A			)
      __A    ='''\n'''.join(__A			)

      # Make a hash from all this code
      __A    =full_str.encode('''utf-8'''			)
      return shaaaa(__A			).hexdigest()


# get importable module names and hash for caching
_lowerCamelCase      :					Dict					    =      {
    '''csv''': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    '''json''': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    '''pandas''': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    '''parquet''': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    '''arrow''': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    '''text''': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    '''imagefolder''': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    '''audiofolder''': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_lowerCamelCase      :					int					    =      {
    '''.csv''': ('''csv''', {}),
    '''.tsv''': ('''csv''', {'''sep''': '''\t'''}),
    '''.json''': ('''json''', {}),
    '''.jsonl''': ('''json''', {}),
    '''.parquet''': ('''parquet''', {}),
    '''.arrow''': ('''arrow''', {}),
    '''.txt''': ('''text''', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_lowerCamelCase      :					List[str]					    =      {'''imagefolder''', '''audiofolder'''}

# Used to filter data files based on extensions given a module name
_lowerCamelCase      :					Dict[str, List[str]]					    =      {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('''.zip''')


_MODULE_TO_EXTENSIONS["audiofolder"].append('''.zip''')


