"""simple docstring"""





from collections import UserDict
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_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

lowerCAmelCase_     			=						logging.get_logger(__name__)




@add_end_docstrings(__a			)
class 			_snake_case		(     __a			):







				"""simple docstring"""






				def __init__(   self				:				List[str]   ,     **_A				:				Dict):



											"""simple docstring"""







											super().__init__(**lowercase_)

											requires_backends(self   ,     """vision""")
											self.check_model_type(
											    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
											    if self.framework == """tf"""
											    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING)





				def __call__(   self				:				Union[str, Any]   ,     _A				:				Union[str, List[str], "Image", List["Image"]]   ,     **_A				:				Dict):



											"""simple docstring"""







											return super().__call__(lowercase_   ,     **lowercase_)





				def 		_lowerCAmelCase		(   self				:				List[Any]   ,     **_A				:				List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     {}
											if "candidate_labels" in kwargs:
																		_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     kwargs["""candidate_labels"""]
											if "hypothesis_template" in kwargs:
																		_SCREAMING_SNAKE_CASE		:    List[Any]      	=     kwargs["""hypothesis_template"""]

											return preprocess_params, {}, {}





				def 		_lowerCAmelCase		(   self				:				Dict   ,     _A				:				Dict   ,     _A				:				Optional[int]=None   ,     _A				:				Dict="This is a photo of {}."):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    str      	=     load_image(lowercase_)
											_SCREAMING_SNAKE_CASE		:    Any      	=     self.image_processor(images=[image]   ,     return_tensors=self.framework)
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     candidate_labels
											_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     [hypothesis_template.format(lowercase_) for x in candidate_labels]
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     self.tokenizer(lowercase_   ,     return_tensors=self.framework   ,     padding=lowercase_)
											_SCREAMING_SNAKE_CASE		:    str      	=     [text_inputs]
											return inputs





				def 		_lowerCAmelCase		(   self				:				Tuple   ,     _A				:				Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Dict      	=     model_inputs.pop("""candidate_labels""")
											_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     model_inputs.pop("""text_inputs""")
											if isinstance(text_inputs[0]   ,     lowercase_):
																		_SCREAMING_SNAKE_CASE		:    List[str]      	=     text_inputs[0]
											else:
																		# Batching case.
																		_SCREAMING_SNAKE_CASE		:    str      	=     text_inputs[0][0]

											_SCREAMING_SNAKE_CASE		:    List[str]      	=     self.model(**lowercase_   ,     **lowercase_)

											_SCREAMING_SNAKE_CASE		:    Tuple      	=     {
											    """candidate_labels""": candidate_labels,
											    """logits""": outputs.logits_per_image,
											}
											return model_outputs





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



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Tuple      	=     model_outputs.pop("""candidate_labels""")
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     model_outputs["""logits"""][0]
											if self.framework == "pt":
																		_SCREAMING_SNAKE_CASE		:    Any      	=     logits.softmax(dim=-1).squeeze(-1)
																		_SCREAMING_SNAKE_CASE		:    Any      	=     probs.tolist()
																		if not isinstance(lowercase_   ,     lowercase_):
																									_SCREAMING_SNAKE_CASE		:    str      	=     [scores]
											elif self.framework == "tf":
																		_SCREAMING_SNAKE_CASE		:    List[str]      	=     stable_softmax(lowercase_   ,     axis=-1)
																		_SCREAMING_SNAKE_CASE		:    Dict      	=     probs.numpy().tolist()
											else:
																		raise ValueError(f"""Unsupported framework: {self.framework}""")

											_SCREAMING_SNAKE_CASE		:    List[str]      	=     [
											    {"""score""": score, """label""": candidate_label}
											    for score, candidate_label in sorted(zip(lowercase_   ,     lowercase_)   ,     key=lambda _A: -x[0])
											]
											return result



'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[Any]:
  lowercase_     =       [
      """encoder.version""",
      """decoder.version""",
      """model.encoder.version""",
      """model.decoder.version""",
      """decoder.output_projection.weight""",
      """_float_tensor""",
      """encoder.embed_positions._float_tensor""",
      """decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    state_dict.pop(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  lowercase_     =       list(s_dict.keys()						)
  for key in keys:
    if "transformer_layers" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)
    elif "subsample" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     ,	lowercase_     =       emb.weight.shape
  lowercase_     =       nn.Linear(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	bias=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       emb.weight.data
  return lin_layer

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  lowercase_     =       torch.load(SCREAMING_SNAKE_CASE_ ,	map_location="""cpu"""						)
  lowercase_     =       mam_aaa["""args"""]
  lowercase_     =       mam_aaa["""model"""]
  lowercase_     =       state_dict["""decoder.output_projection.weight"""]

  remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
  rename_keys(SCREAMING_SNAKE_CASE_						)

  lowercase_     =       state_dict["""decoder.embed_tokens.weight"""].shape[0]

  lowercase_     =       args.share_decoder_input_output_embed

  lowercase_     =       [int(SCREAMING_SNAKE_CASE_						) for i in args.conv_kernel_sizes.split(""","""						)]
  lowercase_     =       SpeechaTextConfig(
      vocab_size=SCREAMING_SNAKE_CASE_ ,	max_source_positions=args.max_source_positions ,	max_target_positions=args.max_target_positions ,	encoder_layers=args.encoder_layers ,	decoder_layers=args.decoder_layers ,	encoder_attention_heads=args.encoder_attention_heads ,	decoder_attention_heads=args.decoder_attention_heads ,	encoder_ffn_dim=args.encoder_ffn_embed_dim ,	decoder_ffn_dim=args.decoder_ffn_embed_dim ,	d_model=args.encoder_embed_dim ,	dropout=args.dropout ,	attention_dropout=args.attention_dropout ,	activation_dropout=args.activation_dropout ,	activation_function="""relu""" ,	num_conv_layers=len(SCREAMING_SNAKE_CASE_						) ,	conv_channels=args.conv_channels ,	conv_kernel_sizes=SCREAMING_SNAKE_CASE_ ,	input_feat_per_channel=args.input_feat_per_channel ,	input_channels=args.input_channels ,	tie_word_embeddings=SCREAMING_SNAKE_CASE_ ,	num_beams=5 ,	max_length=2_00 ,	use_cache=SCREAMING_SNAKE_CASE_ ,	decoder_start_token_id=2 ,	early_stopping=SCREAMING_SNAKE_CASE_ ,	)

  lowercase_     =       SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     =       model.model.load_state_dict(SCREAMING_SNAKE_CASE_ ,	strict=SCREAMING_SNAKE_CASE_						)
  if len(SCREAMING_SNAKE_CASE_						) > 0 and not set(SCREAMING_SNAKE_CASE_						) <= {
      "encoder.embed_positions.weights",
      "decoder.embed_positions.weights",
  }:
    raise ValueError(
        """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
        f""" but all the following weights are missing {missing}"""						)

  if tie_embeds:
    lowercase_     =       make_linear_from_emb(model.model.decoder.embed_tokens						)
  else:
    lowercase_     =       lm_head_weights

  model.save_pretrained(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
       __snake_case						  =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("""--fairseq_path""", type=str, help="""Path to the fairseq model (.pt) file.""")
       parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
       __snake_case						  =		parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

