"""simple docstring"""


import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase__  		=						"""src/diffusers"""
UpperCAmelCase__  		=						"""."""


# This is to make sure the diffusers module imported is the one in the repo.
UpperCAmelCase__  		=						importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(DIFFUSERS_PATH, """__init__.py"""),
    submodule_search_locations=[DIFFUSERS_PATH],
)
UpperCAmelCase__  		=						spec.loader.load_module()






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  return line.startswith(lowercase		) or len(lowercase		) <= 1 or re.search(R"""^\s*\)(\s*->.*:|:)\s*$"""   ,lowercase		) is not None






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      object_name.split("""."""		)
  _UpperCAmelCase						 =      0

  # First let's find the module where our object lives.
  _UpperCAmelCase						 =      parts[i]
  while i < len(lowercase		) and not os.path.isfile(os.path.join(lowercase   ,f'''{module}.py'''		)		):
    i += 1
    if i < len(lowercase		):
      _UpperCAmelCase						 =      os.path.join(lowercase   ,parts[i]		)
  if i >= len(lowercase		):
    raise ValueError(f'''`object_name` should begin with the name of a module of diffusers but got {object_name}.'''		)

  with open(os.path.join(lowercase   ,f'''{module}.py'''		)   ,"""r"""   ,encoding="""utf-8"""   ,newline="""\n"""		) as f:
    _UpperCAmelCase						 =      f.readlines()

  # Now let's find the class / func in the code!
  _UpperCAmelCase						 =      """"""
  _UpperCAmelCase						 =      0
  for name in parts[i + 1 :]:
    while (
        line_index < len(lowercase		) and re.search(Rf'''^{indent}(class|def)\s+{name}(\(|\:)'''   ,lines[line_index]		) is None
    ):
      line_index += 1
    indent += "    "
    line_index += 1

  if line_index >= len(lowercase		):
    raise ValueError(f''' {object_name} does not match any function or class in {module}.'''		)

  # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
  _UpperCAmelCase						 =      line_index
  while line_index < len(lowercase		) and _should_continue(lines[line_index]   ,lowercase		):
    line_index += 1
  # Clean up empty lines at the end (if any).
  while len(lines[line_index - 1]		) <= 1:
    line_index -= 1

  _UpperCAmelCase						 =      lines[start_index:line_index]
  return "".join(lowercase		)


UpperCAmelCase__  		=						re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
UpperCAmelCase__  		=						re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
UpperCAmelCase__  		=						re.compile(r"""<FILL\s+[^>]*>""")






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      code.split("""\n"""		)
  _UpperCAmelCase						 =      0
  while idx < len(lowercase		) and len(lines[idx]		) == 0:
    idx += 1
  if idx < len(lowercase		):
    return re.search(R"""^(\s*)\S"""   ,lines[idx]		).groups()[0]
  return ""






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      len(get_indent(lowercase		)		) > 0
  if has_indent:
    _UpperCAmelCase						 =      f'''class Bla:\n{code}'''
  _UpperCAmelCase						 =      black.Mode(target_versions={black.TargetVersion.PYaa}   ,line_length=1_19   ,preview=lowercase		)
  _UpperCAmelCase						 =      black.format_str(lowercase   ,mode=lowercase		)
  _UpperCAmelCase ,				_UpperCAmelCase						 =      style_docstrings_in_code(lowercase		)
  return result[len("""class Bla:\n"""		) :] if has_indent else result






def     __UpperCAmelCase					(			lowercase   ,lowercase=False		):







  """simple docstring"""
  with open(lowercase   ,"""r"""   ,encoding="""utf-8"""   ,newline="""\n"""		) as f:
    _UpperCAmelCase						 =      f.readlines()
  _UpperCAmelCase						 =      []
  _UpperCAmelCase						 =      0
  # Not a for loop cause `lines` is going to change (if `overwrite=True`).
  while line_index < len(lowercase		):
    _UpperCAmelCase						 =      _re_copy_warning.search(lines[line_index]		)
    if search is None:
      line_index += 1
      continue

    # There is some copied code here, let's retrieve the original.
    _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      search.groups()
    _UpperCAmelCase						 =      find_code_in_diffusers(lowercase		)
    _UpperCAmelCase						 =      get_indent(lowercase		)

    _UpperCAmelCase						 =      line_index + 1 if indent == theoretical_indent else line_index + 2
    _UpperCAmelCase						 =      theoretical_indent
    _UpperCAmelCase						 =      start_index

    # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
    _UpperCAmelCase						 =      True
    while line_index < len(lowercase		) and should_continue:
      line_index += 1
      if line_index >= len(lowercase		):
        break
      _UpperCAmelCase						 =      lines[line_index]
      _UpperCAmelCase						 =      _should_continue(lowercase   ,lowercase		) and re.search(f'''^{indent}# End copy'''   ,lowercase		) is None
    # Clean up empty lines at the end (if any).
    while len(lines[line_index - 1]		) <= 1:
      line_index -= 1

    _UpperCAmelCase						 =      lines[start_index:line_index]
    _UpperCAmelCase						 =      """""".join(lowercase		)

    # Remove any nested `Copied from` comments to avoid circular copies
    _UpperCAmelCase						 =      [line for line in theoretical_code.split("""\n"""		) if _re_copy_warning.search(lowercase		) is None]
    _UpperCAmelCase						 =      """\n""".join(lowercase		)

    # Before comparing, use the `replace_pattern` on the original code.
    if len(lowercase		) > 0:
      _UpperCAmelCase						 =      replace_pattern.replace("""with"""   ,""""""		).split(""","""		)
      _UpperCAmelCase						 =      [_re_replace_pattern.search(lowercase		) for p in patterns]
      for pattern in patterns:
        if pattern is None:
          continue
        _UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      pattern.groups()
        _UpperCAmelCase						 =      re.sub(lowercase   ,lowercase   ,lowercase		)
        if option.strip() == "all-casing":
          _UpperCAmelCase						 =      re.sub(obja.lower()   ,obja.lower()   ,lowercase		)
          _UpperCAmelCase						 =      re.sub(obja.upper()   ,obja.upper()   ,lowercase		)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
      _UpperCAmelCase						 =      blackify(lines[start_index - 1] + theoretical_code		)
      _UpperCAmelCase						 =      theoretical_code[len(lines[start_index - 1]		) :]

    # Test for a diff and act accordingly.
    if observed_code != theoretical_code:
      diffs.append([object_name, start_index]		)
      if overwrite:
        _UpperCAmelCase						 =      lines[:start_index] + [theoretical_code] + lines[line_index:]
        _UpperCAmelCase						 =      start_index + 1

  if overwrite and len(lowercase		) > 0:
    # Warn the user a file has been modified.
    print(f'''Detected changes, rewriting {filename}.'''		)
    with open(lowercase   ,"""w"""   ,encoding="""utf-8"""   ,newline="""\n"""		) as f:
      f.writelines(lowercase		)
  return diffs






def     __UpperCAmelCase					(			lowercase = False		):







  """simple docstring"""
  _UpperCAmelCase						 =      glob.glob(os.path.join(lowercase   ,"""**/*.py"""		)   ,recursive=lowercase		)
  _UpperCAmelCase						 =      []
  for filename in all_files:
    _UpperCAmelCase						 =      is_copy_consistent(lowercase   ,lowercase		)
    diffs += [f'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
  if not overwrite and len(lowercase		) > 0:
    _UpperCAmelCase						 =      """\n""".join(lowercase		)
    raise Exception(
        """Found the following copy inconsistencies:\n"""
        + diff
        + """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."""		)


if __name__ == "__main__":
    UpperCAmelCase__  		=						argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    UpperCAmelCase__  		=						parser.parse_args()

    check_copies(args.fix_and_overwrite)

"""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
