





"""simple docstring"""




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

A   : Tuple			=				logging.get_logger(__name__)


@add_end_docstrings(__lowercase   )
class   A      (__lowercase   ):
			'''simple docstring'''




			def __init__(      self       :   str							,     *__lowerCAmelCase       :   Any							,     **__lowerCAmelCase       :   Tuple       )     ->     List[Any]:







					"""simple docstring"""






					super().__init__(*snake_case_							,     **snake_case_       )
					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       :   Tuple							,     __lowerCAmelCase       :   List[str]=None							,     __lowerCAmelCase       :   Union[str, Any]=None							,     __lowerCAmelCase       :   Any=None       )     ->     str:







					"""simple docstring"""






					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       :   Tuple							,     __lowerCAmelCase       :   Union[str, List[str], "Image.Image", List["Image.Image"]]							,     **__lowerCAmelCase       :   List[str]       )     ->     List[str]:







					"""simple docstring"""






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






			def        a_	(      self       :   List[Any]							,     __lowerCAmelCase       :   Any							,     __lowerCAmelCase       :   List[str]=None       )     ->     List[Any]:







					"""simple docstring"""






					A__  				=       load_image(snake_case_       )

					if prompt is not None:
							if not isinstance(snake_case_							,     snake_case_       ):
									raise ValueError(
									    f'Received an invalid text input, got - {type(snake_case_       )} - 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=snake_case_							,     return_tensors=self.framework       )
									A__  				=       self.tokenizer(text=snake_case_							,     add_special_tokens=snake_case_       ).input_ids
									A__  				=       [self.tokenizer.cls_token_id] + input_ids
									A__  				=       torch.tensor(snake_case_       ).unsqueeze(0       )
									model_inputs.update({"""input_ids""": input_ids}       )

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

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

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

					else:
							A__  				=       self.image_processor(images=snake_case_							,     return_tensors=self.framework       )

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

					return model_inputs






			def        a_	(      self       :   str							,     __lowerCAmelCase       :   Optional[Any]							,     __lowerCAmelCase       :   int=None       )     ->     str:







					"""simple docstring"""






					if (
					    "input_ids" in model_inputs
					    and isinstance(model_inputs["""input_ids"""]							,     snake_case_       )
					    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(snake_case_							,     **snake_case_							,     **snake_case_       )
					return model_outputs







			def        a_	(      self       :   List[Any]							,     __lowerCAmelCase       :   Optional[Any]       )     ->     List[str]:







					"""simple docstring"""






					A__  				=       []
					for output_ids in model_outputs:
							A__  				=       {
							    '''generated_text''': self.tokenizer.decode(
							        snake_case_							,     skip_special_tokens=snake_case_							,     )
							}
							records.append(snake_case_       )
					return records










import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					A   : List[str]			=				argparse.ArgumentParser(
					    description=(
					        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
					        ''' Distillation'''
					    )
					)
					parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
					parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
					parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
					parser.add_argument('''--vocab_transform''', action='''store_true''')
					A   : Tuple			=				parser.parse_args()

					if args.model_type == "bert":
										A   : Dict			=				BertForMaskedLM.from_pretrained(args.model_name)
										A   : List[str]			=				'''bert'''
					else:
										raise ValueError('''args.model_type should be "bert".''')

					A   : Optional[Any]			=				model.state_dict()
					A   : int			=				{}

					for w in ["word_embeddings", "position_embeddings"]:
										A   : str			=				state_dict[F'''{prefix}.embeddings.{w}.weight''']
					for w in ["weight", "bias"]:
										A   : Any			=				state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

					A   : Tuple			=				0
					for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
										for w in ["weight", "bias"]:
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
															]

															A   : int			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
															]
															A   : List[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
															]

															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
															]
															A   : Union[str, Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
															]
															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
															]
										std_idx += 1

					A   : int			=				state_dict['''cls.predictions.decoder.weight''']
					A   : str			=				state_dict['''cls.predictions.bias''']
					if args.vocab_transform:
										for w in ["weight", "bias"]:
															A   : List[Any]			=				state_dict[F'''cls.predictions.transform.dense.{w}''']
															A   : List[str]			=				state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

					print(F'''N layers selected for distillation: {std_idx}''')
					print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

					print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
					torch.save(compressed_sd, args.dump_checkpoint)



