

import argparse
import os
import re


__UpperCAmelCase								=			"""src/transformers"""

# Pattern that looks at the indentation in a line.
__UpperCAmelCase								=			re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
__UpperCAmelCase								=			re.compile(R"""^\s*\"([^\"]+)\":""")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__UpperCAmelCase								=			re.compile(R"""^\s*_import_structure\[\"([^\"]+)\"\]""")
# Pattern that matches `"key",` and puts `key` in group 0.
__UpperCAmelCase								=			re.compile(R"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__UpperCAmelCase								=			re.compile(R"""\[([^\]]+)\]""")





def      snake_case_				(__A					:						int					)      ->     int:
   __lowerCAmelCase  :      Union[str, Any]    =						_re_indent.search(__A					)
   return "" if search is None else search.groups()[0]





def      snake_case_				(__A					:						Union[str, Any] ,       __A					:						Union[str, Any]="" ,       __A					:						List[Any]=None ,       __A					:						str=None					)      ->     Dict:
   __lowerCAmelCase  :      Tuple    =						0
   __lowerCAmelCase  :      int    =						code.split("""\n"""					)
   if start_prompt is not None:
      while not lines[index].startswith(__A					):
         index += 1
      __lowerCAmelCase  :      Optional[int]    =						["""\n""".join(lines[:index]					)]
   else:
      __lowerCAmelCase  :      Optional[int]    =						[]

   # We split into blocks until we get to the `end_prompt` (or the end of the block).
   __lowerCAmelCase  :      Dict    =						[lines[index]]
   index += 1
   while index < len(__A					) and (end_prompt is None or not lines[index].startswith(__A					)):
      if len(lines[index]					) > 0 and get_indent(lines[index]					) == indent_level:
         if len(__A					) > 0 and get_indent(current_block[-1]					).startswith(indent_level + """ """					):
            current_block.append(lines[index]					)
            blocks.append("""\n""".join(__A					)					)
            if index < len(__A					) - 1:
               __lowerCAmelCase  :      Optional[int]    =						[lines[index + 1]]
               index += 1
            else:
               __lowerCAmelCase  :      List[str]    =						[]
         else:
            blocks.append("""\n""".join(__A					)					)
            __lowerCAmelCase  :      str    =						[lines[index]]
      else:
         current_block.append(lines[index]					)
      index += 1

   # Adds current block if it's nonempty.
   if len(__A					) > 0:
      blocks.append("""\n""".join(__A					)					)

   # Add final block after end_prompt if provided.
   if end_prompt is not None and index < len(__A					):
      blocks.append("""\n""".join(lines[index:]					)					)

   return blocks





def      snake_case_				(__A					:						List[Any]					)      ->     str:

   def _inner(__A					:						List[str]					):
      return key(__A					).lower().replace("""_""" ,       """"""					)

   return _inner





def      snake_case_				(__A					:						str ,       __A					:						List[str]=None					)      ->     int:

   # If no key is provided, we use a noop.
   def noop(__A					:						List[Any]					):
      return x

   if key is None:
      __lowerCAmelCase  :      List[str]    =						noop
   # Constants are all uppercase, they go first.
   __lowerCAmelCase  :      List[str]    =						[obj for obj in objects if key(__A					).isupper()]
   # Classes are not all uppercase but start with a capital, they go second.
   __lowerCAmelCase  :      int    =						[obj for obj in objects if key(__A					)[0].isupper() and not key(__A					).isupper()]
   # Functions begin with a lowercase, they go last.
   __lowerCAmelCase  :      str    =						[obj for obj in objects if not key(__A					)[0].isupper()]

   __lowerCAmelCase  :      Any    =						ignore_underscore(__A					)
   return sorted(__A ,       key=__A					) + sorted(__A ,       key=__A					) + sorted(__A ,       key=__A					)





def      snake_case_				(__A					:						int					)      ->     Dict:

   # This inner function sort imports between [ ].
   def _replace(__A					:						int					):
      __lowerCAmelCase  :      Optional[int]    =						match.groups()[0]
      if "," not in imports:
         return f'''[{imports}]'''
      __lowerCAmelCase  :      str    =						[part.strip().replace("""\"""" ,       """"""					) for part in imports.split(""","""					)]
      # We will have a final empty element if the line finished with a comma.
      if len(keys[-1]					) == 0:
         __lowerCAmelCase  :      Tuple    =						keys[:-1]
      return "[" + ", ".join([f'''"{k}"''' for k in sort_objects(__A					)]					) + "]"

   __lowerCAmelCase  :      int    =						import_statement.split("""\n"""					)
   if len(__A					) > 3:
      # Here we have to sort internal imports that are on several lines (one per name):
      # key: [
      #     "object1",
      #     "object2",
      #     ...
      # ]

      # We may have to ignore one or two lines on each side.
      __lowerCAmelCase  :      List[Any]    =						2 if lines[1].strip() == """[""" else 1
      __lowerCAmelCase  :      Union[str, Any]    =						[(i, _re_strip_line.search(__A					).groups()[0]) for i, line in enumerate(lines[idx:-idx]					)]
      __lowerCAmelCase  :      Union[str, Any]    =						sort_objects(__A ,       key=lambda __A					: x[1]					)
      __lowerCAmelCase  :      Optional[int]    =						[lines[x[0] + idx] for x in sorted_indices]
      return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]					)
   elif len(__A					) == 3:
      # Here we have to sort internal imports that are on one separate line:
      # key: [
      #     "object1", "object2", ...
      # ]
      if _re_bracket_content.search(lines[1]					) is not None:
         __lowerCAmelCase  :      List[Any]    =						_re_bracket_content.sub(_replace ,       lines[1]					)
      else:
         __lowerCAmelCase  :      Dict    =						[part.strip().replace("""\"""" ,       """"""					) for part in lines[1].split(""","""					)]
         # We will have a final empty element if the line finished with a comma.
         if len(keys[-1]					) == 0:
            __lowerCAmelCase  :      Tuple    =						keys[:-1]
         __lowerCAmelCase  :      int    =						get_indent(lines[1]					) + """, """.join([f'''"{k}"''' for k in sort_objects(__A					)]					)
      return "\n".join(__A					)
   else:
      # Finally we have to deal with imports fitting on one line
      __lowerCAmelCase  :      Optional[Any]    =						_re_bracket_content.sub(_replace ,       __A					)
      return import_statement





def      snake_case_				(__A					:						int ,       __A					:						str=True					)      ->     Any:
   with open(__A ,       encoding="""utf-8"""					) as f:
      __lowerCAmelCase  :      Optional[int]    =						f.read()

   if "_import_structure" not in code:
      return

   # Blocks of indent level 0
   __lowerCAmelCase  :      List[Any]    =						split_code_in_indented_blocks(
       __A ,       start_prompt="""_import_structure = {""" ,       end_prompt="""if TYPE_CHECKING:"""					)

   # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
   for block_idx in range(1 ,       len(__A					) - 1					):
      # Check if the block contains some `_import_structure`s thingy to sort.
      __lowerCAmelCase  :      Optional[Any]    =						main_blocks[block_idx]
      __lowerCAmelCase  :      str    =						block.split("""\n"""					)

      # Get to the start of the imports.
      __lowerCAmelCase  :      Tuple    =						0
      while line_idx < len(__A					) and "_import_structure" not in block_lines[line_idx]:
         # Skip dummy import blocks
         if "import dummy" in block_lines[line_idx]:
            __lowerCAmelCase  :      Union[str, Any]    =						len(__A					)
         else:
            line_idx += 1
      if line_idx >= len(__A					):
         continue

      # Ignore beginning and last line: they don't contain anything.
      __lowerCAmelCase  :      Optional[int]    =						"""\n""".join(block_lines[line_idx:-1]					)
      __lowerCAmelCase  :      List[str]    =						get_indent(block_lines[1]					)
      # Slit the internal block into blocks of indent level 1.
      __lowerCAmelCase  :      int    =						split_code_in_indented_blocks(__A ,       indent_level=__A					)
      # We have two categories of import key: list or _import_structure[key].append/extend
      __lowerCAmelCase  :      Dict    =						_re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
      # Grab the keys, but there is a trap: some lines are empty or just comments.
      __lowerCAmelCase  :      List[str]    =						[(pattern.search(__A					).groups()[0] if pattern.search(__A					) is not None else None) for b in internal_blocks]
      # We only sort the lines with a key.
      __lowerCAmelCase  :      List[str]    =						[(i, key) for i, key in enumerate(__A					) if key is not None]
      __lowerCAmelCase  :      int    =						[x[0] for x in sorted(__A ,       key=lambda __A					: x[1]					)]

      # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
      __lowerCAmelCase  :      Optional[Any]    =						0
      __lowerCAmelCase  :      str    =						[]
      for i in range(len(__A					)					):
         if keys[i] is None:
            reorderded_blocks.append(internal_blocks[i]					)
         else:
            __lowerCAmelCase  :      Optional[Any]    =						sort_objects_in_import(internal_blocks[sorted_indices[count]]					)
            reorderded_blocks.append(__A					)
            count += 1

        # And we put our main block back together with its first and last line.
      __lowerCAmelCase  :      str    =						"""\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]					)

   if code != "\n".join(__A					):
      if check_only:
         return True
      else:
         print(f'''Overwriting {file}.'''					)
         with open(__A ,       """w""" ,       encoding="""utf-8"""					) as f:
            f.write("""\n""".join(__A					)					)





def      snake_case_				(__A					:						Tuple=True					)      ->     Union[str, Any]:
   __lowerCAmelCase  :      List[str]    =						[]
   for root, _, files in os.walk(__A					):
      if "__init__.py" in files:
         __lowerCAmelCase  :      Any    =						sort_imports(os.path.join(__A ,       """__init__.py"""					) ,       check_only=__A					)
         if result:
            __lowerCAmelCase  :      Any    =						[os.path.join(__A ,       """__init__.py"""					)]
   if len(__A					) > 0:
      raise ValueError(f'''Would overwrite {len(__A					)} files, run `make style`.'''					)


if __name__ == "__main__":
   __UpperCAmelCase								=			argparse.ArgumentParser()
   parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
   __UpperCAmelCase								=			parser.parse_args()

   sort_imports_in_all_inits(check_only=args.check_only)





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 SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										=MBartConfig
  lowerCamelCase   :      Optional[Any]										={}
  lowerCamelCase   :      Dict										="gelu"







  def __init__(      self			:   str	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   List[Any]=13	,							lowerCAmelCase			:   List[str]=7	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Optional[Any]=False	,							lowerCAmelCase			:   Union[str, Any]=99	,							lowerCAmelCase			:   List[str]=32	,							lowerCAmelCase			:   List[Any]=2	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   Any=37	,							lowerCAmelCase			:   Optional[int]=0.1	,							lowerCAmelCase			:   List[Any]=0.1	,							lowerCAmelCase			:   Dict=20	,							lowerCAmelCase			:   Any=2	,							lowerCAmelCase			:   Union[str, Any]=1	,							lowerCAmelCase			:   str=0	,							)							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						parent
     __lowerCAmelCase  :      int    =						batch_size
     __lowerCAmelCase  :      int    =						seq_length
     __lowerCAmelCase  :      Tuple    =						is_training
     __lowerCAmelCase  :      Optional[int]    =						use_labels
     __lowerCAmelCase  :      str    =						vocab_size
     __lowerCAmelCase  :      List[str]    =						hidden_size
     __lowerCAmelCase  :      Dict    =						num_hidden_layers
     __lowerCAmelCase  :      int    =						num_attention_heads
     __lowerCAmelCase  :      Any    =						intermediate_size
     __lowerCAmelCase  :      Dict    =						hidden_dropout_prob
     __lowerCAmelCase  :      List[Any]    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Tuple    =						max_position_embeddings
     __lowerCAmelCase  :      Union[str, Any]    =						eos_token_id
     __lowerCAmelCase  :      Optional[Any]    =						pad_token_id
     __lowerCAmelCase  :      int    =						bos_token_id







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







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

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

     __lowerCAmelCase  :      Dict    =						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  :      Tuple    =						prepare_mbart_inputs_dict(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )
     return config, inputs_dict







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   str   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						TFMBartModel(config=lowerCAmelCase   ).get_decoder()
     __lowerCAmelCase  :      Tuple    =						inputs_dict["""input_ids"""]

     __lowerCAmelCase  :      Optional[Any]    =						input_ids[:1, :]
     __lowerCAmelCase  :      Union[str, Any]    =						inputs_dict["""attention_mask"""][:1, :]
     __lowerCAmelCase  :      Tuple    =						inputs_dict["""head_mask"""]
     __lowerCAmelCase  :      Any    =						1

     # first forward pass
     __lowerCAmelCase  :      List[Any]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							head_mask=lowerCAmelCase	,							use_cache=lowerCAmelCase   )

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






def      snake_case_				(__A					:						str ,       __A					:						Union[str, Any] ,       __A					:						Tuple ,       __A					:						Tuple=None ,       __A					:						Optional[Any]=None ,       __A					:						Optional[Any]=None ,       __A					:						Optional[int]=None ,       __A					:						Optional[Any]=None ,       )      ->     int:
   if attention_mask is None:
      __lowerCAmelCase  :      Dict    =						tf.cast(tf.math.not_equal(__A ,       config.pad_token_id					) ,       tf.inta					)
   if decoder_attention_mask is None:
      __lowerCAmelCase  :      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:
      __lowerCAmelCase  :      Tuple    =						tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
   if decoder_head_mask is None:
      __lowerCAmelCase  :      List[Any]    =						tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
   if cross_attn_head_mask is None:
      __lowerCAmelCase  :      Any    =						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 SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






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







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Optional[Any]   )							->     Optional[int]:






     """simple docstring"""







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

     return False







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						TFMBartModelTester(self   )
     __lowerCAmelCase  :      Union[str, Any]    =						ConfigTester(self	,							config_class=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     List[Any]:






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						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 SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






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







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Any:






     """simple docstring"""







     return AutoTokenizer.from_pretrained(self.model_name   )







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
     return model







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							**lowerCAmelCase			:   Dict   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						self.translate_src_text(**lowerCAmelCase   )
     self.assertListEqual(self.expected_text	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							**lowerCAmelCase			:   int   )							->     str:






     """simple docstring"""







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







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Any:






     """simple docstring"""







     self._assert_generated_batch_equal_expected()


