






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   :   int      =    logging.get_logger(__name__)


@add_end_docstrings(_lowerCAmelCase )
class 							__UpperCamelCase     (  _lowerCAmelCase ):







	def __init__(	self   :				Any							,					*_lowerCAmelCase   :				Any							,					**_lowerCAmelCase   :				int      )		->			Dict:
		"""simple docstring"""







		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 			_a (	self   :				Optional[Any]							,					_lowerCAmelCase   :				List[Any]=None							,					_lowerCAmelCase   :				Any=None							,					_lowerCAmelCase   :				Dict=None      )		->			int:
		"""simple docstring"""







		__lowercase   							=	{}
		__lowercase   							=	{}

		if prompt is not None:
			__lowercase   							=	prompt

		if generate_kwargs is not None:
			__lowercase   							=	generate_kwargs
		if max_new_tokens is not None:
			if "generate_kwargs" not in forward_kwargs:
				__lowercase   							=	{}
			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"""      )
			__lowercase   							=	max_new_tokens
		return preprocess_params, forward_kwargs, {}







	def __call__(	self   :				Union[str, Any]							,					_lowerCAmelCase   :				Union[str, List[str], "Image.Image", List["Image.Image"]]							,					**_lowerCAmelCase   :				Any      )		->			Tuple:
		"""simple docstring"""







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







	def 			_a (	self   :				int							,					_lowerCAmelCase   :				Tuple							,					_lowerCAmelCase   :				Any=None      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	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."""      )

			__lowercase   							=	self.model.config.model_type

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

			elif model_type == "pix2struct":
				__lowercase   							=	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
				__lowercase   							=	self.image_processor(images=_lowerCAmelCase							,					return_tensors=self.framework      )
				__lowercase   							=	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:
			__lowercase   							=	self.image_processor(images=_lowerCAmelCase							,					return_tensors=self.framework      )

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

		return model_inputs







	def 			_a (	self   :				List[Any]							,					_lowerCAmelCase   :				Tuple							,					_lowerCAmelCase   :				List[str]=None      )		->			Tuple:
		"""simple docstring"""







		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"""]      )
		):
			__lowercase   							=	None

		if generate_kwargs is None:
			__lowercase   							=	{}
		# 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.
		__lowercase   							=	model_inputs.pop(self.model.main_input_name      )
		__lowercase   							=	self.model.generate(_lowerCAmelCase							,					**_lowerCAmelCase							,					**_lowerCAmelCase      )
		return model_outputs







	def 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				Union[str, Any]      )		->			int:
		"""simple docstring"""







		__lowercase   							=	[]
		for output_ids in model_outputs:
			__lowercase   							=	{
			    """generated_text""": self.tokenizer.decode(
			        _lowerCAmelCase							,					skip_special_tokens=_lowerCAmelCase							,					)
			}
			records.append(_lowerCAmelCase      )
		return records







'''simple docstring'''





import argparse

import torch

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


logging.set_verbosity_info()
UpperCamelCase_		:     int          =		logging.get_logger(__name__)

UpperCamelCase_		:     Tuple          =		[
    ['''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      (  _UpperCamelCase:       List[Any]       )		->					Union[str, Any]:

     """simple docstring"""




     if k == "embeddings.weight":
          return "shared.weight"

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

     if k.startswith("encoder"       ):
          _snake_case				= k.replace(".attn"   ,		".self_attn"       )
          _snake_case				= k.replace("norm1"   ,		"self_attn_layer_norm"       )
          _snake_case				= k.replace("norm2"   ,		"final_layer_norm"       )
     elif k.startswith("decoder"       ):
          _snake_case				= k.replace("norm1"   ,		"self_attn_layer_norm"       )
          _snake_case				= k.replace("norm2"   ,		"encoder_attn_layer_norm"       )
          _snake_case				= k.replace("norm3"   ,		"final_layer_norm"       )
     return k





def       __a      (  _UpperCamelCase:       Dict       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= [
         "model.encoder.layernorm_embedding.weight",
         "model.encoder.layernorm_embedding.bias",
         "model.decoder.layernorm_embedding.weight",
         "model.decoder.layernorm_embedding.bias",
     ]
     for k in keys:
          _snake_case				= sd.pop(_UpperCamelCase       )
          _snake_case				= k.replace("layernorm_embedding"   ,		"layer_norm"       )
          assert new_k not in sd
          _snake_case				= v


UpperCamelCase_		:     Union[str, Any]          =		['''START''']





@torch.no_grad()
def       __a      (  _UpperCamelCase:       Any   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Union[str, Any]       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= torch.load(_UpperCamelCase   ,		map_location="cpu"       )
     _snake_case				= model["model"]
     _snake_case				= BlenderbotConfig.from_json_file(_UpperCamelCase       )
     _snake_case				= BlenderbotForConditionalGeneration(_UpperCamelCase       )
     _snake_case				= m.model.state_dict().keys()
     _snake_case				= []
     _snake_case				= {}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

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


if __name__ == "__main__":
   UpperCamelCase_		:     Any          =		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_		:     Optional[int]          =		parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


