






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__							  =						"src/transformers"
UpperCAmelCase__							  =						"docs/source/en/tasks"
def 		_a       ( a					:Union[str, Any]      ,				a					:Tuple      ,				a					:Tuple					)				->     Union[str, Any]:
 with open(a      ,				'''r'''      ,				encoding='''utf-8'''      ,				newline='''\n'''					) as f:
  a						    = f.readlines()
 # Find the start prompt.
 a						    = 0
 while not lines[start_index].startswith(a					):
  start_index += 1
 start_index += 1

 a						    = 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__							  =						direct_transformers_import(TRANSFORMERS_PATH)

UpperCAmelCase__							  =						{
    "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__							  =						{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}
def 		_a       ( a					:Tuple					)				->     Tuple:
 a						    = TASK_GUIDE_TO_MODELS[task_guide]
 a						    = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a      ,				set()					)
 a						    = {
     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       ( a					:Any      ,				a					:List[Any]=False					)				->     Any:
 a    ,	a    ,	a    ,	a						    = _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-->'''      ,				)

 a						    = 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__							  =						argparse.ArgumentParser()
     parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
     UpperCAmelCase__							  =						parser.parse_args()

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










import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__							  =						logging.get_logger(__name__)

UpperCAmelCase__							  =						[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]
def 		_a       ( a					:Optional[Any]					)				->     List[Any]:
 if k == "embeddings.weight":
  return "shared.weight"

 for parlai_name, hf_name in PATTERNS:
  a						    = k.replace(a      ,				a					)

 if k.startswith('''encoder'''					):
  a						    = k.replace('''.attn'''      ,				'''.self_attn'''					)
  a						    = k.replace('''norm1'''      ,				'''self_attn_layer_norm'''					)
  a						    = k.replace('''norm2'''      ,				'''final_layer_norm'''					)
 elif k.startswith('''decoder'''					):
  a						    = k.replace('''norm1'''      ,				'''self_attn_layer_norm'''					)
  a						    = k.replace('''norm2'''      ,				'''encoder_attn_layer_norm'''					)
  a						    = k.replace('''norm3'''      ,				'''final_layer_norm'''					)
 return k
def 		_a       ( a					:Dict					)				->     Tuple:
 a						    = [
     '''model.encoder.layernorm_embedding.weight''',
     '''model.encoder.layernorm_embedding.bias''',
     '''model.decoder.layernorm_embedding.weight''',
     '''model.decoder.layernorm_embedding.bias''',
 ]
 for k in keys:
  a						    = sd.pop(a					)
  a						    = k.replace('''layernorm_embedding'''      ,				'''layer_norm'''					)
  assert new_k not in sd
  a						    = v


UpperCAmelCase__							  =						["START"]
@torch.no_grad()
def 		_a       ( a					:Dict      ,				a					:str      ,				a					:int					)				->     int:
 a						    = torch.load(a      ,				map_location='''cpu'''					)
 a						    = model['''model''']
 a						    = BlenderbotConfig.from_json_file(a					)
 a						    = BlenderbotForConditionalGeneration(a					)
 a						    = m.model.state_dict().keys()
 a						    = []
 a						    = {}
 for k, v in sd.items():
  if k in IGNORE_KEYS:
   continue

  a						    = rename_state_dict_key(a					)
  if new_k not in valid_keys:
   failures.append([k, new_k]					)
  else:
   a						    = v
 if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
  rename_layernorm_keys(a					)
 m.model.load_state_dict(a      ,				strict=a					)
 m.half()
 m.save_pretrained(a					)


if __name__ == "__main__":
     UpperCAmelCase__							  =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
     parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
     parser.add_argument(
         "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
     )
     UpperCAmelCase__							  =						parser.parse_args()
     convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


