


import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


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

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







def        lowerCAmelCase_  (      __lowerCamelCase			):
							return 1.0 / (1.0 + np.exp(-_outputs			))







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    str											=     np.max(_outputs							,			axis=-1							,			keepdims=__lowerCamelCase			)
							__snake_case	:    Optional[int]											=     np.exp(_outputs - maxes			)
							return shifted_exp / shifted_exp.sum(axis=-1							,			keepdims=__lowerCamelCase			)

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							List[Any]  						=						"sigmoid"
							__UpperCAmelCase					:							Union[str, Any]  						=						"softmax"
							__UpperCAmelCase					:							Tuple  						=						"none"

@add_end_docstrings(
    _lowerCAmelCase  ,       R"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "  ,       )
class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							int  						=						False
							__UpperCAmelCase					:							Dict  						=						ClassificationFunction.NONE

							def __init__(							self :						str					,			**lowerCamelCase :						Union[str, Any]       )			->					List[str]:
														super().__init__(**lowerCamelCase       )

														self.check_model_type(
														    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
														    if self.framework == "tf"
														    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING       )

							def 					__snake_case      (							self :						str					,			lowerCamelCase :						Any=None					,			lowerCamelCase :						Dict=None					,			lowerCamelCase :						str=""					,			**lowerCamelCase :						Optional[Any]       )			->					List[str]:
														# Using "" as default argument because we're going to use `top_k=None` in user code to declare
														# "No top_k"
														__snake_case	:    Any											=     tokenizer_kwargs

														__snake_case	:    Tuple											=     {}
														if hasattr(self.model.config					,			"return_all_scores"       ) and return_all_scores is None:
																					__snake_case	:    Union[str, Any]											=     self.model.config.return_all_scores

														if isinstance(lowerCamelCase					,			lowerCamelCase       ) or top_k is None:
																					__snake_case	:    Any											=     top_k
																					__snake_case	:    str											=     False
														elif return_all_scores is not None:
																					warnings.warn(
																					    "`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"
																					    " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."					,			lowerCamelCase					,			)
																					if return_all_scores:
																												__snake_case	:    List[Any]											=     None
																					else:
																												__snake_case	:    Optional[Any]											=     1

														if isinstance(lowerCamelCase					,			lowerCamelCase       ):
																					__snake_case	:    List[str]											=     ClassificationFunction[function_to_apply.upper()]

														if function_to_apply is not None:
																					__snake_case	:    List[Any]											=     function_to_apply
														return preprocess_params, {}, postprocess_params

							def __call__(							self :						Tuple					,			*lowerCamelCase :						Union[str, Any]					,			**lowerCamelCase :						Tuple       )			->					Union[str, Any]:
														__snake_case	:    str											=     super().__call__(*lowerCamelCase					,			**lowerCamelCase       )
														# TODO try and retrieve it in a nicer way from _sanitize_parameters.
														__snake_case	:    Dict											=     "top_k" not in kwargs
														if isinstance(args[0]					,			lowerCamelCase       ) and _legacy:
																					# This pipeline is odd, and return a list when single item is run
																					return [result]
														else:
																					return result

							def 					__snake_case      (							self :						int					,			lowerCamelCase :						int					,			**lowerCamelCase :						Tuple       )			->					Dict[str, GenericTensor]:
														__snake_case	:    List[str]											=     self.framework
														if isinstance(lowerCamelCase					,			lowerCamelCase       ):
																					return self.tokenizer(**lowerCamelCase					,			return_tensors=lowerCamelCase					,			**lowerCamelCase       )
														elif isinstance(lowerCamelCase					,			lowerCamelCase       ) and len(lowerCamelCase       ) == 1 and isinstance(inputs[0]					,			lowerCamelCase       ) and len(inputs[0]       ) == 2:
																					# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
																					return self.tokenizer(
																					    text=inputs[0][0]					,			text_pair=inputs[0][1]					,			return_tensors=lowerCamelCase					,			**lowerCamelCase       )
														elif isinstance(lowerCamelCase					,			lowerCamelCase       ):
																					# This is likely an invalid usage of the pipeline attempting to pass text pairs.
																					raise ValueError(
																					    "The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
																					    " dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."       )
														return self.tokenizer(lowerCamelCase					,			return_tensors=lowerCamelCase					,			**lowerCamelCase       )

							def 					__snake_case      (							self :						Tuple					,			lowerCamelCase :						int       )			->					List[Any]:
														return self.model(**lowerCamelCase       )






							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						List[str]=None					,			lowerCamelCase :						Optional[int]=1					,			lowerCamelCase :						Dict=True       )			->					Dict:
														# `_legacy` is used to determine if we're running the naked pipeline and in backward
														# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
														# the more natural result containing the list.
														# Default value before `set_parameters`
														if function_to_apply is None:
																					if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																												__snake_case	:    Union[str, Any]											=     ClassificationFunction.SIGMOID
																					elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																												__snake_case	:    str											=     ClassificationFunction.SOFTMAX
																					elif hasattr(self.model.config					,			"function_to_apply"       ) and function_to_apply is None:
																												__snake_case	:    Tuple											=     self.model.config.function_to_apply
																					else:
																												__snake_case	:    str											=     ClassificationFunction.NONE

														__snake_case	:    Optional[int]											=     model_outputs["logits"][0]
														__snake_case	:    Union[str, Any]											=     outputs.numpy()

														if function_to_apply == ClassificationFunction.SIGMOID:
																					__snake_case	:    List[Any]											=     sigmoid(lowerCamelCase       )
														elif function_to_apply == ClassificationFunction.SOFTMAX:
																					__snake_case	:    Any											=     softmax(lowerCamelCase       )
														elif function_to_apply == ClassificationFunction.NONE:
																					__snake_case	:    Union[str, Any]											=     outputs
														else:
																					raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}'       )

														if top_k == 1 and _legacy:
																					return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

														__snake_case	:    str											=     [
														    {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(lowerCamelCase       )
														]
														if not _legacy:
																					dict_scores.sort(key=lambda lowerCamelCase       : x["score"]					,			reverse=lowerCamelCase       )
																					if top_k is not None:
																												__snake_case	:    Tuple											=     dict_scores[:top_k]
														return dict_scores





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
_snake_case  :	List[Any]			      =       "src/diffusers"
_snake_case  :	str			      =       "."


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







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







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    str											=     object_name.split("."			)
							__snake_case	:    Any											=     0

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

							with open(os.path.join(__lowerCamelCase							,			F'{module}.py'			)							,			"r"							,			encoding="utf-8"							,			newline="\n"			) as f:
														__snake_case	:    Optional[Any]											=     f.readlines()

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

							__snake_case	:    Dict											=     lines[start_index:line_index]
							return "".join(__lowerCamelCase			)


_snake_case  :	Any			      =       re.compile(R"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
_snake_case  :	Union[str, Any]			      =       re.compile(R"^\s*(\S+)->(\S+)(\s+.*|$)")
_snake_case  :	Optional[int]			      =       re.compile(R"<FILL\s+[^>]*>")







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    Dict											=     code.split("\n"			)
							__snake_case	:    List[str]											=     0
							while idx < len(__lowerCamelCase			) and len(lines[idx]			) == 0:
														idx += 1
							if idx < len(__lowerCamelCase			):
														return re.search(R"^(\s*)\S"							,			lines[idx]			).groups()[0]
							return ""







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    List[str]											=     len(get_indent(__lowerCamelCase			)			) > 0
							if has_indent:
														__snake_case	:    str											=     F'class Bla:\n{code}'
							__snake_case	:    List[Any]											=     black.Mode(target_versions={black.TargetVersion.PYaa}							,			line_length=1_1_9							,			preview=__lowerCamelCase			)
							__snake_case	:    int											=     black.format_str(__lowerCamelCase							,			mode=__lowerCamelCase			)
							__snake_case				,    __snake_case	:    List[str]											=     style_docstrings_in_code(__lowerCamelCase			)
							return result[len("class Bla:\n"			) :] if has_indent else result







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=False			):
							with open(__lowerCamelCase							,			"r"							,			encoding="utf-8"							,			newline="\n"			) as f:
														__snake_case	:    List[Any]											=     f.readlines()
							__snake_case	:    int											=     []
							__snake_case	:    Optional[Any]											=     0
							# Not a for loop cause `lines` is going to change (if `overwrite=True`).
							while line_index < len(__lowerCamelCase			):
														__snake_case	:    List[Any]											=     _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.
														__snake_case				,    __snake_case				,    __snake_case	:    List[str]											=     search.groups()
														__snake_case	:    List[str]											=     find_code_in_diffusers(__lowerCamelCase			)
														__snake_case	:    str											=     get_indent(__lowerCamelCase			)

														__snake_case	:    Optional[Any]											=     line_index + 1 if indent == theoretical_indent else line_index + 2
														__snake_case	:    Tuple											=     theoretical_indent
														__snake_case	:    str											=     start_index

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

														__snake_case	:    int											=     lines[start_index:line_index]
														__snake_case	:    int											=     "".join(__lowerCamelCase			)

														# Remove any nested `Copied from` comments to avoid circular copies
														__snake_case	:    Union[str, Any]											=     [line for line in theoretical_code.split("\n"			) if _re_copy_warning.search(__lowerCamelCase			) is None]
														__snake_case	:    Optional[Any]											=     "\n".join(__lowerCamelCase			)

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

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

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







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

			check_copies(args.fix_and_overwrite)

