






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   :   int      =    """src/diffusers"""
__UpperCamelCase   :   Dict      =    """."""


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





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







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





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	object_name.split("""."""       )
	__lowercase   							=	0

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

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

	# Now let's find the class / func in the code!
	__lowercase   							=	""""""
	__lowercase   							=	0
	for name in parts[i + 1 :]:
		while (
		    line_index < len(_snake_case       ) 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(_snake_case       ):
		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).
	__lowercase   							=	line_index
	while line_index < len(_snake_case       ) and _should_continue(lines[line_index]					,	_snake_case       ):
		line_index += 1
	# Clean up empty lines at the end (if any).
	while len(lines[line_index - 1]       ) <= 1:
		line_index -= 1

	__lowercase   							=	lines[start_index:line_index]
	return "".join(_snake_case       )


__UpperCamelCase   :   Dict      =    re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
__UpperCamelCase   :   Optional[Any]      =    re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
__UpperCamelCase   :   List[str]      =    re.compile(r"""<FILL\s+[^>]*>""")





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	code.split("""\n"""       )
	__lowercase   							=	0
	while idx < len(_snake_case       ) and len(lines[idx]       ) == 0:
		idx += 1
	if idx < len(_snake_case       ):
		return re.search(r"""^(\s*)\S"""					,	lines[idx]       ).groups()[0]
	return ""





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	len(get_indent(_snake_case       )       ) > 0
	if has_indent:
		__lowercase   							=	F'class Bla:\n{code}'
	__lowercase   							=	black.Mode(target_versions={black.TargetVersion.PYaa}					,	line_length=119					,	preview=_snake_case       )
	__lowercase   							=	black.format_str(_snake_case					,	mode=_snake_case       )
	__lowercase							,    __lowercase   							=	style_docstrings_in_code(_snake_case       )
	return result[len("""class Bla:\n"""       ) :] if has_indent else result





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase=False       ):




	'''simple docstring'''







	with open(_snake_case					,	"""r"""					,	encoding="""utf-8"""					,	newline="""\n"""       ) as f:
		__lowercase   							=	f.readlines()
	__lowercase   							=	[]
	__lowercase   							=	0
	# Not a for loop cause `lines` is going to change (if `overwrite=True`).
	while line_index < len(_snake_case       ):
		__lowercase   							=	_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.
		__lowercase							,    __lowercase							,    __lowercase   							=	search.groups()
		__lowercase   							=	find_code_in_diffusers(_snake_case       )
		__lowercase   							=	get_indent(_snake_case       )

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

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

		__lowercase   							=	lines[start_index:line_index]
		__lowercase   							=	"""""".join(_snake_case       )

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

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

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

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





def 					snake_case	(  lowerCamelCase = False       ):




	'''simple docstring'''







	__lowercase   							=	glob.glob(os.path.join(_snake_case					,	"""**/*.py"""       )					,	recursive=_snake_case       )
	__lowercase   							=	[]
	for filename in all_files:
		__lowercase   							=	is_copy_consistent(_snake_case					,	_snake_case       )
		diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
	if not overwrite and len(_snake_case       ) > 0:
		__lowercase   							=	"""\n""".join(_snake_case       )
		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   :   List[str]      =    argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						__UpperCamelCase   :   Dict      =    parser.parse_args()

						check_copies(args.fix_and_overwrite)






from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

UpperCAmelCase_		 =					logging.get_logger(__name__)


@add_end_docstrings(_A)
class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						*__lowerCAmelCase					:					List[Any]			,						**__lowerCAmelCase					:					List[str]      )   ->	List[str]:
    super().__init__(*__lowerCAmelCase			,						**__lowerCAmelCase      )
    requires_backends(self			,						'''vision'''      )
    self.check_model_type(
        TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING      )

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Dict=None			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Optional[Any]=None      )   ->	int:
    _A      =       {}
    _A      =       {}

    if prompt is not None:
       _A      =       prompt

    if generate_kwargs is not None:
       _A      =       generate_kwargs
    if max_new_tokens is not None:
       if "generate_kwargs" not in forward_kwargs:
          _A      =       {}
       if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
          raise ValueError(
              '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
              ''' please use only one'''      )
       _A      =       max_new_tokens
    return preprocess_params, forward_kwargs, {}

 def __call__(    self					:					List[str]			,						__lowerCAmelCase					:					Union[str, List[str], "Image.Image", List["Image.Image"]]			,						**__lowerCAmelCase					:					Union[str, Any]      )   ->	Union[str, Any]:
    return super().__call__(__lowerCAmelCase			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Union[str, Any]=None      )   ->	int:
    _A      =       load_image(__lowerCAmelCase      )

    if prompt is not None:
       if not isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
          raise ValueError(
              f'''Received an invalid text input, got - {type(__lowerCAmelCase      )} - but expected a single string. '''
              '''Note also that one single text can be provided for conditional image to text generation.'''      )

       _A      =       self.model.config.model_type

       if model_type == "git":
          _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )
          _A      =       self.tokenizer(text=__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase      ).input_ids
          _A      =       [self.tokenizer.cls_token_id] + input_ids
          _A      =       torch.tensor(__lowerCAmelCase      ).unsqueeze(0      )
          model_inputs.update({'''input_ids''': input_ids}      )

       elif model_type == "pix2struct":
          _A      =       self.image_processor(images=__lowerCAmelCase			,						header_text=__lowerCAmelCase			,						return_tensors=self.framework      )

       elif model_type != "vision-encoder-decoder":
          # vision-encoder-decoder does not support conditional generation
          _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )
          _A      =       self.tokenizer(__lowerCAmelCase			,						return_tensors=self.framework      )
          model_inputs.update(__lowerCAmelCase      )

       else:
          raise ValueError(f'''Model type {model_type} does not support conditional text generation'''      )

    else:
       _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )

    if self.model.config.model_type == "git" and prompt is None:
       _A      =       None

    return model_inputs

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Dict=None      )   ->	str:
    # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
    # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
    if (
        "input_ids" in model_inputs
        and isinstance(model_inputs['''input_ids''']			,						__lowerCAmelCase      )
        and all(x is None for x in model_inputs['''input_ids''']      )
    ):
       _A      =       None

    if generate_kwargs is None:
       _A      =       {}
    # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
    #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
    #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
    #  in the `_prepare_model_inputs` method.
    _A      =       model_inputs.pop(self.model.main_input_name      )
    _A      =       self.model.generate(__lowerCAmelCase			,						**__lowerCAmelCase			,						**__lowerCAmelCase      )
    return model_outputs

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Any      )   ->	Union[str, Any]:
    _A      =       []
    for output_ids in model_outputs:
       _A      =       {
           '''generated_text''': self.tokenizer.decode(
               __lowerCAmelCase			,						skip_special_tokens=__lowerCAmelCase			,						)
       }
       records.append(__lowerCAmelCase      )
    return records



