



import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]				)  ->     Dict:
   _A      =       [
       '''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(_snake_case	,   _snake_case				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict				)  ->     Tuple:
   _A      =       list(s_dict.keys()				)
   for key in keys:
      if "transformer_layers" in key:
         _A      =       s_dict.pop(_snake_case				)
      elif "subsample" in key:
         _A      =       s_dict.pop(_snake_case				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[int]				)  ->     str:
   _A  ,    _A      =       emb.weight.shape
   _A      =       nn.Linear(_snake_case	,   _snake_case	,   bias=_snake_case				)
   _A      =       emb.weight.data
   return lin_layer

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]	,   _snake_case	:Union[str, Any]				)  ->     Optional[Any]:
   _A      =       torch.load(_snake_case	,   map_location='''cpu'''				)
   _A      =       mam_aaa['''args''']
   _A      =       mam_aaa['''model''']
   _A      =       state_dict['''decoder.output_projection.weight''']

   remove_ignore_keys_(_snake_case				)
   rename_keys(_snake_case				)

   _A      =       state_dict['''decoder.embed_tokens.weight'''].shape[0]

   _A      =       args.share_decoder_input_output_embed

   _A      =       [int(_snake_case				) for i in args.conv_kernel_sizes.split(''','''				)]
   _A      =       SpeechaTextConfig(
       vocab_size=_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(_snake_case				)	,   conv_channels=args.conv_channels	,   conv_kernel_sizes=_snake_case	,   input_feat_per_channel=args.input_feat_per_channel	,   input_channels=args.input_channels	,   tie_word_embeddings=_snake_case	,   num_beams=5	,   max_length=200	,   use_cache=_snake_case	,   decoder_start_token_id=2	,   early_stopping=_snake_case	,   )

   _A      =       SpeechaTextForConditionalGeneration(_snake_case				)
   _A  ,    _A      =       model.model.load_state_dict(_snake_case	,   strict=_snake_case				)
   if len(_snake_case				) > 0 and not set(_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:
      _A      =       make_linear_from_emb(model.model.decoder.embed_tokens				)
   else:
      _A      =       lm_head_weights

   model.save_pretrained(_snake_case				)


if __name__ == "__main__":
   UpperCAmelCase_		 =					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.""")
   UpperCAmelCase_		 =					parser.parse_args()
   convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)








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_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

UpperCAmelCase_		 =					logging.get_logger(__name__)


@add_end_docstrings(_A)
class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						*__lowerCAmelCase					:					List[Any]			,						**__lowerCAmelCase					:					List[str]      )   ->	List[str]:
    super().__init__(*__lowerCAmelCase			,						**__lowerCAmelCase      )
    requires_backends(self			,						'''vision'''      )
    self.check_model_type(
        TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING      )

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Dict=None			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Optional[Any]=None      )   ->	int:
    _A      =       {}
    _A      =       {}

    if prompt is not None:
       _A      =       prompt

    if generate_kwargs is not None:
       _A      =       generate_kwargs
    if max_new_tokens is not None:
       if "generate_kwargs" not in forward_kwargs:
          _A      =       {}
       if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
          raise ValueError(
              '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
              ''' please use only one'''      )
       _A      =       max_new_tokens
    return preprocess_params, forward_kwargs, {}

 def __call__(    self					:					List[str]			,						__lowerCAmelCase					:					Union[str, List[str], "Image.Image", List["Image.Image"]]			,						**__lowerCAmelCase					:					Union[str, Any]      )   ->	Union[str, Any]:
    return super().__call__(__lowerCAmelCase			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Union[str, Any]=None      )   ->	int:
    _A      =       load_image(__lowerCAmelCase      )

    if prompt is not None:
       if not isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
          raise ValueError(
              f'''Received an invalid text input, got - {type(__lowerCAmelCase      )} - but expected a single string. '''
              '''Note also that one single text can be provided for conditional image to text generation.'''      )

       _A      =       self.model.config.model_type

       if model_type == "git":
          _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )
          _A      =       self.tokenizer(text=__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase      ).input_ids
          _A      =       [self.tokenizer.cls_token_id] + input_ids
          _A      =       torch.tensor(__lowerCAmelCase      ).unsqueeze(0      )
          model_inputs.update({'''input_ids''': input_ids}      )

       elif model_type == "pix2struct":
          _A      =       self.image_processor(images=__lowerCAmelCase			,						header_text=__lowerCAmelCase			,						return_tensors=self.framework      )

       elif model_type != "vision-encoder-decoder":
          # vision-encoder-decoder does not support conditional generation
          _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )
          _A      =       self.tokenizer(__lowerCAmelCase			,						return_tensors=self.framework      )
          model_inputs.update(__lowerCAmelCase      )

       else:
          raise ValueError(f'''Model type {model_type} does not support conditional text generation'''      )

    else:
       _A      =       self.image_processor(images=__lowerCAmelCase			,						return_tensors=self.framework      )

    if self.model.config.model_type == "git" and prompt is None:
       _A      =       None

    return model_inputs

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Dict=None      )   ->	str:
    # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
    # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
    if (
        "input_ids" in model_inputs
        and isinstance(model_inputs['''input_ids''']			,						__lowerCAmelCase      )
        and all(x is None for x in model_inputs['''input_ids''']      )
    ):
       _A      =       None

    if generate_kwargs is None:
       _A      =       {}
    # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
    #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
    #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
    #  in the `_prepare_model_inputs` method.
    _A      =       model_inputs.pop(self.model.main_input_name      )
    _A      =       self.model.generate(__lowerCAmelCase			,						**__lowerCAmelCase			,						**__lowerCAmelCase      )
    return model_outputs

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Any      )   ->	Union[str, Any]:
    _A      =       []
    for output_ids in model_outputs:
       _A      =       {
           '''generated_text''': self.tokenizer.decode(
               __lowerCAmelCase			,						skip_special_tokens=__lowerCAmelCase			,						)
       }
       records.append(__lowerCAmelCase      )
    return records



