






'''simple docstring'''





import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
a_      = 'src/diffusers'
a_      = 'docs/source/en'
a_      = '.'



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						with open(lowercase__	,"r"	,encoding="utf-8"	,newline="\n"				) as f:
												a_				     =   f.readlines()
						# Find the start prompt.
						a_				     =   0
						while not lines[start_index].startswith(lowercase__				):
												start_index += 1
						start_index += 1

						a_				     =   start_index
						while not lines[end_index].startswith(lowercase__				):
												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


# Add here suffixes that are used to identify models, separated by |
a_      = 'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
a_      = re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
a_      = re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
a_      = re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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



def 			__UpperCAmelCase				(lowercase__				)		->				str:

						'''simple docstring'''


						a_				     =   re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"	,lowercase__				)
						return [m.group(0				) for m in matches]



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				Optional[Any]:

						'''simple docstring'''


						a_				     =   2 if text == "✅" or text == "❌" else len(lowercase__				)
						a_				     =   (width - text_length) // 2
						a_				     =   width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent



def 			__UpperCAmelCase				()		->				int:

						'''simple docstring'''


						a_				     =   diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						a_				     =   {
						    name: config_mapping_names[code]
						    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_mapping_names
						}
						a_				     =   {name: config.replace("ConfigMixin"	,""				) for name, config in model_name_to_config.items()}

						# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
						a_				     =   collections.defaultdict(lowercase__				)
						a_				     =   collections.defaultdict(lowercase__				)
						a_				     =   collections.defaultdict(lowercase__				)
						a_				     =   collections.defaultdict(lowercase__				)
						a_				     =   collections.defaultdict(lowercase__				)

						# Let's lookup through all diffusers object (once).
						for attr_name in dir(lowercase__				):
												a_				     =   None
												if attr_name.endswith("Tokenizer"				):
																		a_				     =   slow_tokenizers
																		a_				     =   attr_name[:-9]
												elif attr_name.endswith("TokenizerFast"				):
																		a_				     =   fast_tokenizers
																		a_				     =   attr_name[:-13]
												elif _re_tf_models.match(lowercase__				) is not None:
																		a_				     =   tf_models
																		a_				     =   _re_tf_models.match(lowercase__				).groups()[0]
												elif _re_flax_models.match(lowercase__				) is not None:
																		a_				     =   flax_models
																		a_				     =   _re_flax_models.match(lowercase__				).groups()[0]
												elif _re_pt_models.match(lowercase__				) is not None:
																		a_				     =   pt_models
																		a_				     =   _re_pt_models.match(lowercase__				).groups()[0]

												if lookup_dict is not None:
																		while len(lowercase__				) > 0:
																								if attr_name in model_name_to_prefix.values():
																														a_				     =   True
																														break
																								# Try again after removing the last word in the name
																								a_				     =   "".join(camel_case_split(lowercase__				)[:-1]				)

    # Let's build that table!
						a_				     =   list(model_name_to_config.keys()				)
						model_names.sort(key=str.lower				)
						a_				     =   ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
						# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
						a_				     =   [len(lowercase__				) + 2 for c in columns]
						a_				     =   max([len(lowercase__				) for name in model_names]				) + 2

						# Build the table per se
						a_				     =   "|" + "|".join([_center_text(lowercase__	,lowercase__				) for c, w in zip(lowercase__	,lowercase__				)]				) + "|\n"
						# Use ":-----:" format to center-aligned table cell texts
						table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]				) + "|\n"

						a_				     =   {True: "✅", False: "❌"}
						for name in model_names:
												a_				     =   model_name_to_prefix[name]
												a_				     =   [
												    name,
												    check[slow_tokenizers[prefix]],
												    check[fast_tokenizers[prefix]],
												    check[pt_models[prefix]],
												    check[tf_models[prefix]],
												    check[flax_models[prefix]],
												]
												table += "|" + "|".join([_center_text(lowercase__	,lowercase__				) for l, w in zip(lowercase__	,lowercase__				)]				) + "|\n"
						return table



def 			__UpperCAmelCase				(lowercase__=False				)		->				str:

						'''simple docstring'''


						a_     ,       a_     ,       a_     ,       a_				     =   _find_text_in_file(
						    filename=os.path.join(lowercase__	,"index.mdx"				)	,start_prompt="<!--This table is updated automatically from the auto modules"	,end_prompt="<!-- End table-->"	,)
						a_				     =   get_model_table_from_auto_modules()

						if current_table != new_table:
												if overwrite:
																		with open(os.path.join(lowercase__	,"index.mdx"				)	,"w"	,encoding="utf-8"	,newline="\n"				) as f:
																								f.writelines(lines[:start_index] + [new_table] + lines[end_index:]				)
												else:
																		raise ValueError(
																		    "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."				)


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

							check_model_table(args.fix_and_overwrite)











'''simple docstring'''





import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


a_      = collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)



def 			__UpperCAmelCase				(lowercase__				)		->				CheckpointState:

						'''simple docstring'''


						print(F"""Reading saved model from {model_file}"""				)
						a_				     =   torch.load(lowercase__	,map_location=lambda lowercase__	,lowercase__				: default_restore_location(lowercase__	,"cpu"				)				)
						return CheckpointState(**lowercase__				)





class 		SCREAMING_SNAKE_CASE__   :



		def __init__(    self:    int				,   a:    Path)  ->str:






								'''simple docstring'''


								a_				     =   src_file



		def _lowerCAmelCase     (    self:    Dict)  ->Optional[Any]:






								'''simple docstring'''


								raise NotImplementedError




		@staticmethod
		def _lowerCAmelCase     (    a:    str				,   *a:    int				,   **a:    int)  ->"DPRState":






								'''simple docstring'''


								if comp_type.startswith("c"):
														return DPRContextEncoderState(*a				,   **a)
								if comp_type.startswith("q"):
														return DPRQuestionEncoderState(*a				,   **a)
								if comp_type.startswith("r"):
														return DPRReaderState(*a				,   **a)
								else:
														raise ValueError("Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'.")





class 		SCREAMING_SNAKE_CASE__   (     lowercase_    ):



		def _lowerCAmelCase     (    self:    Union[str, Any])  ->Any:






								'''simple docstring'''


								a_				     =   DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased")[0]))
								print(f"""Loading DPR biencoder from {self.src_file}""")
								a_				     =   load_states_from_checkpoint(self.src_file)
								a_     ,       a_				     =   model.ctx_encoder, "ctx_model."
								# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
								a_				     =   {"bert_model.embeddings.position_ids": model.ctx_encoder.bert_model.embeddings.position_ids}
								for key, value in saved_state.model_dict.items():
														if key.startswith(a):
																				a_				     =   key[len(a) :]
																				if not key.startswith("encode_proj."):
																										a_				     =   "bert_model." + key
																				a_				     =   value
								encoder.load_state_dict(a)
								return model





class 		SCREAMING_SNAKE_CASE__   (     lowercase_    ):



		def _lowerCAmelCase     (    self:    Optional[int])  ->Optional[int]:






								'''simple docstring'''


								a_				     =   DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased")[0]))
								print(f"""Loading DPR biencoder from {self.src_file}""")
								a_				     =   load_states_from_checkpoint(self.src_file)
								a_     ,       a_				     =   model.question_encoder, "question_model."
								# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
								a_				     =   {"bert_model.embeddings.position_ids": model.question_encoder.bert_model.embeddings.position_ids}
								for key, value in saved_state.model_dict.items():
														if key.startswith(a):
																				a_				     =   key[len(a) :]
																				if not key.startswith("encode_proj."):
																										a_				     =   "bert_model." + key
																				a_				     =   value
								encoder.load_state_dict(a)
								return model





class 		SCREAMING_SNAKE_CASE__   (     lowercase_    ):



		def _lowerCAmelCase     (    self:    Tuple)  ->Optional[Any]:






								'''simple docstring'''


								a_				     =   DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased")[0]))
								print(f"""Loading DPR reader from {self.src_file}""")
								a_				     =   load_states_from_checkpoint(self.src_file)
								# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
								a_				     =   {
								    "encoder.bert_model.embeddings.position_ids": model.span_predictor.encoder.bert_model.embeddings.position_ids
								}
								for key, value in saved_state.model_dict.items():
														if key.startswith("encoder.") and not key.startswith("encoder.encode_proj"):
																				a_				     =   "encoder.bert_model." + key[len("encoder.") :]
														a_				     =   value
								model.span_predictor.load_state_dict(a)
								return model





def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				Any:

						'''simple docstring'''


						a_				     =   Path(lowercase__				)
						dest_dir.mkdir(exist_ok=lowercase__				)

						a_				     =   DPRState.from_type(lowercase__	,src_file=lowercase__				)
						a_				     =   dpr_state.load_dpr_model()
						model.save_pretrained(lowercase__				)
						model.from_pretrained(lowercase__				)  # sanity check


if __name__ == "__main__":
							a_      = argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--type', type=str, help='Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'
							)
							parser.add_argument(
							    '--src',
							    type=str,
							    help=(
							        'Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'
							        ' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'
							        ' \'retriever\' checkpoints.'
							    ),
							)
							parser.add_argument('--dest', type=str, default=None, help='Path to the output PyTorch model directory.')
							a_      = parser.parse_args()

							a_      = Path(args.src)
							a_      = F'converted-{src_file.name}' if args.dest is None else args.dest
							a_      = Path(dest_dir)
							assert src_file.exists()
							assert (
							    args.type is not None
							), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
							convert(args.type, src_file, dest_dir)



