






'''simple docstring'''







import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCamelCase    :      List[Any]  			=		'src/transformers'
UpperCamelCase    :      Optional[int]  			=		'docs/source/en/tasks'
def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Optional[int]   ,    __lowerCAmelCase		:			int						):
						with open(a_   ,    """r"""   ,    encoding="""utf-8"""   ,    newline="""\n"""						) as f:
												lowerCamelCase__         =  f.readlines()
						# Find the start prompt.
						lowerCamelCase__         =  0
						while not lines[start_index].startswith(a_						):
												start_index += 1
						start_index += 1

						lowerCamelCase__         =  start_index
						while not lines[end_index].startswith(a_						):
												end_index += 1
						end_index -= 1

						while len(lines[start_index]						) <= 1:
												start_index += 1
						while len(lines[end_index]						) <= 1:
												end_index -= 1
						end_index += 1
						return "".join(lines[start_index:end_index]						), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
UpperCamelCase    :      List[Any]  			=		direct_transformers_import(TRANSFORMERS_PATH)

UpperCamelCase    :      int  			=		{
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
UpperCamelCase    :      Optional[Any]  			=		{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}
def 						A__			(				__lowerCAmelCase		:			List[str]						):
						lowerCamelCase__         =  TASK_GUIDE_TO_MODELS[task_guide]
						lowerCamelCase__         =  SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a_   ,    set()						)
						lowerCamelCase__         =  {
						    code: name
						    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
						    if (code in model_maping_names or code in special_model_types)
						}
						return ", ".join([F'''[{name}](../model_doc/{code})''' for code, name in model_names.items()]						) + "\n"
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Optional[Any]=False						):
						lowerCamelCase__         =  _find_text_in_file(
						    filename=os.path.join(a_   ,    a_						)   ,    start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"""   ,    end_prompt="""<!--End of the generated tip-->"""   ,    )

						lowerCamelCase__         =  get_model_list_for_task(a_						)

						if current_list != new_list:
												if overwrite:
																		with open(os.path.join(a_   ,    a_						)   ,    """w"""   ,    encoding="""utf-8"""   ,    newline="""\n"""						) as f:
																								f.writelines(lines[:start_index] + [new_list] + lines[end_index:]						)
												else:
																		raise ValueError(
																		    F'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
																		    """ to fix this."""						)


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

	for task_guide in TASK_GUIDE_TO_MODELS.keys():
		check_model_list_for_task(task_guide, args.fix_and_overwrite)









'''simple docstring'''







import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase    :      Union[str, Any]  			=		logging.get_logger(__name__)
def 						A__			(				__lowerCAmelCase		:			int						):
						lowerCamelCase__         =  R"""\w+[.]\d+"""
						lowerCamelCase__         =  re.findall(__lowerCAmelCase   ,    __lowerCAmelCase						)
						for pat in pats:
												lowerCamelCase__         =  key.replace(__lowerCAmelCase   ,    """_""".join(pat.split("""."""						)						)						)
						return key
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Tuple						):
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""scale""",)
						if (
						    any("""norm""" in str_ for str_ in pt_tuple_key						)
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												lowerCamelCase__         =  pt_tuple_key[:-1] + ("""scale""",)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												lowerCamelCase__         =  pt_tuple_key[:-1] + ("""scale""",)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												lowerCamelCase__         =  pt_tuple_key[:-1] + ("""embedding""",)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""kernel""",)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												lowerCamelCase__         =  pt_tensor.transpose(2   ,    3   ,    1   ,    0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""kernel""",)
						if pt_tuple_key[-1] == "weight":
												lowerCamelCase__         =  pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""weight""",)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""bias""",)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor





def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Tuple=42						):
						# Step 1: Convert pytorch tensor to numpy
						lowerCamelCase__         =  {k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						lowerCamelCase__         =  flax_model.init_weights(PRNGKey(__lowerCAmelCase						)						)

						lowerCamelCase__         =  flatten_dict(__lowerCAmelCase						)
						lowerCamelCase__         =  {}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												lowerCamelCase__         =  rename_key(__lowerCAmelCase						)
												lowerCamelCase__         =  tuple(renamed_pt_key.split("""."""						)						)

												# Correctly rename weight parameters
												lowerCamelCase__       ,			lowerCamelCase__         =  rename_key_and_reshape_tensor(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																								    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)

        # also add unexpected weight so that warning is thrown
												lowerCamelCase__         =  jnp.asarray(__lowerCAmelCase						)

						return unflatten_dict(__lowerCAmelCase						)

