"""simple docstring"""


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     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      _re_indent.search(lowercase		)
  return "" if search is None else search.groups()[0]






def     __UpperCAmelCase					(			lowercase   ,lowercase=""   ,lowercase=None   ,lowercase=None		):







  """simple docstring"""
  _UpperCAmelCase						 =      0
  _UpperCAmelCase						 =      code.split("""\n"""		)
  if start_prompt is not None:
    while not lines[index].startswith(lowercase		):
      index += 1
    _UpperCAmelCase						 =      ["""\n""".join(lines[:index]		)]
  else:
    _UpperCAmelCase						 =      []

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

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

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

  return blocks






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""

  def _inner(lowercase		):
    return key(lowercase		).lower().replace("""_"""   ,""""""		)

  return _inner






def     __UpperCAmelCase					(			lowercase   ,lowercase=None		):







  """simple docstring"""

  # If no key is provided, we use a noop.
  def noop(lowercase		):
    return x

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

  _UpperCAmelCase						 =      ignore_underscore(lowercase		)
  return sorted(lowercase   ,key=lowercase		) + sorted(lowercase   ,key=lowercase		) + sorted(lowercase   ,key=lowercase		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""

  # This inner function sort imports between [ ].
  def _replace(lowercase		):
    _UpperCAmelCase						 =      match.groups()[0]
    if "," not in imports:
      return f'''[{imports}]'''
    _UpperCAmelCase						 =      [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:
      _UpperCAmelCase						 =      keys[:-1]
    return "[" + ", ".join([f'''"{k}"''' for k in sort_objects(lowercase		)]		) + "]"

  _UpperCAmelCase						 =      import_statement.split("""\n"""		)
  if len(lowercase		) > 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.
    _UpperCAmelCase						 =      2 if lines[1].strip() == """[""" else 1
    _UpperCAmelCase						 =      [(i, _re_strip_line.search(lowercase		).groups()[0]) for i, line in enumerate(lines[idx:-idx]		)]
    _UpperCAmelCase						 =      sort_objects(lowercase   ,key=lambda lowercase		: x[1]		)
    _UpperCAmelCase						 =      [lines[x[0] + idx] for x in sorted_indices]
    return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]		)
  elif len(lowercase		) == 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:
      _UpperCAmelCase						 =      _re_bracket_content.sub(_replace   ,lines[1]		)
    else:
      _UpperCAmelCase						 =      [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:
        _UpperCAmelCase						 =      keys[:-1]
      _UpperCAmelCase						 =      get_indent(lines[1]		) + """, """.join([f'''"{k}"''' for k in sort_objects(lowercase		)]		)
    return "\n".join(lowercase		)
  else:
    # Finally we have to deal with imports fitting on one line
    _UpperCAmelCase						 =      _re_bracket_content.sub(_replace   ,lowercase		)
    return import_statement






def     __UpperCAmelCase					(			lowercase   ,lowercase=True		):







  """simple docstring"""
  with open(lowercase   ,encoding="""utf-8"""		) as f:
    _UpperCAmelCase						 =      f.read()

  if "_import_structure" not in code:
    return

  # Blocks of indent level 0
  _UpperCAmelCase						 =      split_code_in_indented_blocks(
      lowercase   ,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(lowercase		) - 1		):
    # Check if the block contains some `_import_structure`s thingy to sort.
    _UpperCAmelCase						 =      main_blocks[block_idx]
    _UpperCAmelCase						 =      block.split("""\n"""		)

    # Get to the start of the imports.
    _UpperCAmelCase						 =      0
    while line_idx < len(lowercase		) and "_import_structure" not in block_lines[line_idx]:
      # Skip dummy import blocks
      if "import dummy" in block_lines[line_idx]:
        _UpperCAmelCase						 =      len(lowercase		)
      else:
        line_idx += 1
    if line_idx >= len(lowercase		):
      continue

    # Ignore beginning and last line: they don't contain anything.
    _UpperCAmelCase						 =      """\n""".join(block_lines[line_idx:-1]		)
    _UpperCAmelCase						 =      get_indent(block_lines[1]		)
    # Slit the internal block into blocks of indent level 1.
    _UpperCAmelCase						 =      split_code_in_indented_blocks(lowercase   ,indent_level=lowercase		)
    # We have two categories of import key: list or _import_structure[key].append/extend
    _UpperCAmelCase						 =      _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.
    _UpperCAmelCase						 =      [(pattern.search(lowercase		).groups()[0] if pattern.search(lowercase		) is not None else None) for b in internal_blocks]
    # We only sort the lines with a key.
    _UpperCAmelCase						 =      [(i, key) for i, key in enumerate(lowercase		) if key is not None]
    _UpperCAmelCase						 =      [x[0] for x in sorted(lowercase   ,key=lambda lowercase		: x[1]		)]

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

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

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






def     __UpperCAmelCase					(			lowercase=True		):







  """simple docstring"""
  _UpperCAmelCase						 =      []
  for root, _, files in os.walk(lowercase		):
    if "__init__.py" in files:
      _UpperCAmelCase						 =      sort_imports(os.path.join(lowercase   ,"""__init__.py"""		)   ,check_only=lowercase		)
      if result:
        _UpperCAmelCase						 =      [os.path.join(lowercase   ,"""__init__.py"""		)]
  if len(lowercase		) > 0:
    raise ValueError(f'''Would overwrite {len(lowercase		)} 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)

"""simple docstring"""


import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf

if is_torch_available():
    import torch


UpperCAmelCase__  		=						logging.get_logger(__name__)


class        a				:


    def __init__(					self	:  Tuple   ,				__lowerCAmelCase	:  str = None   ,				__lowerCAmelCase	:  uuid.UUID = None   ,				__lowerCAmelCase	:  Any=None   ,				__lowerCAmelCase	:  Tuple=None					):
      if not conversation_id:
        _UpperCAmelCase						 =      uuid.uuida()
      if past_user_inputs is None:
        _UpperCAmelCase						 =      []
      if generated_responses is None:
        _UpperCAmelCase						 =      []

      _UpperCAmelCase						 =      conversation_id
      _UpperCAmelCase						 =      past_user_inputs
      _UpperCAmelCase						 =      generated_responses
      _UpperCAmelCase						 =      text


    def __eq__(					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Union[str, Any]					):
      if not isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
        return False
      if self.uuid == other.uuid:
        return True
      return (
          self.new_user_input == other.new_user_input
          and self.past_user_inputs == other.past_user_inputs
          and self.generated_responses == other.generated_responses
      )


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  bool = False					):
      if self.new_user_input:
        if overwrite:
          logger.warning(
              f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
              f'''with: "{text}".'''					)
          _UpperCAmelCase						 =      text
        else:
          logger.warning(
              f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
              f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''					)
      else:
        _UpperCAmelCase						 =      text


    def      lowerCAmelCase_  (					self	:  int					):
      if self.new_user_input:
        self.past_user_inputs.append(self.new_user_input					)
      _UpperCAmelCase						 =      None


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  str					):
      self.generated_responses.append(__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      for user_input, generated_response in zip(self.past_user_inputs   ,				self.generated_responses					):
        yield True, user_input
        yield False, generated_response
      if self.new_user_input:
        yield True, self.new_user_input


    def __repr__(					self	:  Optional[int]					):
      _UpperCAmelCase						 =      f'''Conversation id: {self.uuid} \n'''
      for is_user, text in self.iter_texts():
        _UpperCAmelCase						 =      """user""" if is_user else """bot"""
        output += f'''{name} >> {text} \n'''
      return output



@add_end_docstrings(
    lowerCAmelCase_	,							R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '	,							)
class        a				(		lowerCAmelCase_   ):


    def __init__(					self	:  List[Any]   ,				*__lowerCAmelCase	:  List[Any]   ,				**__lowerCAmelCase	:  Tuple					):
      super().__init__(*__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if self.tokenizer.pad_token_id is None:
        _UpperCAmelCase						 =      self.tokenizer.eos_token


    def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Dict=None   ,				__lowerCAmelCase	:  List[Any]=None   ,				__lowerCAmelCase	:  Optional[int]=None   ,				**__lowerCAmelCase	:  str					):
      _UpperCAmelCase						 =      {}
      _UpperCAmelCase						 =      {}
      _UpperCAmelCase						 =      {}

      if min_length_for_response is not None:
        _UpperCAmelCase						 =      min_length_for_response
      if minimum_tokens is not None:
        _UpperCAmelCase						 =      minimum_tokens

      if "max_length" in generate_kwargs:
        _UpperCAmelCase						 =      generate_kwargs["""max_length"""]
        # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
      if clean_up_tokenization_spaces is not None:
        _UpperCAmelCase						 =      clean_up_tokenization_spaces

      if generate_kwargs:
        forward_params.update(__lowerCAmelCase					)
      return preprocess_params, forward_params, postprocess_params


    def __call__(					self	:  Tuple   ,				__lowerCAmelCase	:  Union[Conversation, List[Conversation]]   ,				__lowerCAmelCase	:  int=0   ,				**__lowerCAmelCase	:  List[str]					):
      _UpperCAmelCase						 =      super().__call__(__lowerCAmelCase   ,				num_workers=__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					) and len(__lowerCAmelCase					) == 1:
        return outputs[0]
      return outputs


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  Conversation   ,				__lowerCAmelCase	:  Optional[int]=32					):
      if not isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
        raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""					)
      if conversation.new_user_input is None:
        raise ValueError(
            f'''Conversation with UUID {type(conversation.uuid					)} does not contain new user input to process. '''
            """Add user inputs with the conversation's `add_user_input` method"""					)
      if hasattr(self.tokenizer   ,				"""_build_conversation_input_ids"""					):
        _UpperCAmelCase						 =      self.tokenizer._build_conversation_input_ids(__lowerCAmelCase					)
      else:
        # If the tokenizer cannot handle conversations, we default to only the old version
        _UpperCAmelCase						 =      self._legacy_parse_and_tokenize(__lowerCAmelCase					)

      if self.framework == "pt":
        _UpperCAmelCase						 =      torch.LongTensor([input_ids]					)
      elif self.framework == "tf":
        _UpperCAmelCase						 =      tf.constant([input_ids]					)
      return {"input_ids": input_ids, "conversation": conversation}


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[str]=10   ,				**__lowerCAmelCase	:  Union[str, Any]					):
      _UpperCAmelCase						 =      generate_kwargs.get("""max_length"""   ,				self.model.config.max_length					)

      _UpperCAmelCase						 =      model_inputs["""input_ids"""].shape[1]
      if max_length - minimum_tokens < n:
        logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''					)
        _UpperCAmelCase						 =      max_length - minimum_tokens
        _UpperCAmelCase						 =      model_inputs["""input_ids"""][:, -trim:]
        if "attention_mask" in model_inputs:
          _UpperCAmelCase						 =      model_inputs["""attention_mask"""][:, -trim:]
      _UpperCAmelCase						 =      model_inputs.pop("""conversation"""					)
      _UpperCAmelCase						 =      max_length
      _UpperCAmelCase						 =      self.model.generate(**__lowerCAmelCase   ,				**__lowerCAmelCase					)
      if self.model.config.is_encoder_decoder:
        _UpperCAmelCase						 =      1
      else:
        _UpperCAmelCase						 =      n
      return {"output_ids": output_ids[:, start_position:], "conversation": conversation}


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[Any]=True					):
      _UpperCAmelCase						 =      model_outputs["""output_ids"""]
      _UpperCAmelCase						 =      self.tokenizer.decode(
          output_ids[0]   ,				skip_special_tokens=__lowerCAmelCase   ,				clean_up_tokenization_spaces=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      model_outputs["""conversation"""]
      conversation.mark_processed()
      conversation.append_response(__lowerCAmelCase					)
      return conversation


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  Conversation					):
      _UpperCAmelCase						 =      self.tokenizer.eos_token_id
      _UpperCAmelCase						 =      []
      for is_user, text in conversation.iter_texts():
        if eos_token_id is not None:
          input_ids.extend(self.tokenizer.encode(__lowerCAmelCase   ,				add_special_tokens=__lowerCAmelCase					) + [eos_token_id]					)
        else:
          input_ids.extend(self.tokenizer.encode(__lowerCAmelCase   ,				add_special_tokens=__lowerCAmelCase					)					)

      if len(__lowerCAmelCase					) > self.tokenizer.model_max_length:
        _UpperCAmelCase						 =      input_ids[-self.tokenizer.model_max_length :]
      return input_ids
