






import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  checkpoints.load_tax_checkpoint(snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  flatten_dict(snake_case__		)
						return flax_params

def 	__lowerCamelCase      (		snake_case__		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  {}

						_SCREAMING_SNAKE_CASE     				=  {
						    """token_embedder""": """embeddings""",
						    """encoder_norm""": """layernorm""",
						    """kernel""": """weight""",
						    """.out""": """.output""",
						    """scale""": """weight""",
						    """embedders_0.pos_embedding""": """row_embedder.weight""",
						    """embedders_1.pos_embedding""": """column_embedder.weight""",
						}

						_SCREAMING_SNAKE_CASE     				=  {
						    """query""": """attention.query""",
						    """key""": """attention.key""",
						    """value""": """attention.value""",
						    """output.dense""": """output""",
						    """encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
						    """pre_self_attention_layer_norm""": """self_attention.layer_norm""",
						    """pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
						    """mlp.""": """mlp.DenseReluDense.""",
						    """pre_mlp_layer_norm""": """mlp.layer_norm""",
						    """self_attention.o""": """self_attention.attention.o""",
						    """decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
						    """decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
						    """decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
						    """decoder.logits_dense.weight""": """decoder.lm_head.weight""",
						}

						for key in flax_dict.keys():
												if "target" in key:
																		# remove the first prefix from the key
																		_SCREAMING_SNAKE_CASE     				=  """.""".join(key[1:]		)

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								_SCREAMING_SNAKE_CASE     				=  new_key.replace(snake_case__	,snake_case__		)

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														_SCREAMING_SNAKE_CASE     				=  new_key.replace(snake_case__	,snake_case__		)

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								_SCREAMING_SNAKE_CASE     				=  re.sub(r"""layers_(\d+)"""	,r"""layer.\1"""	,snake_case__		)
																								_SCREAMING_SNAKE_CASE     				=  new_key.replace("""encoder"""	,"""encoder.encoder"""		)

																		elif "layers" in new_key and "decoder" in new_key:
																								# use regex to replace the layer number
																								_SCREAMING_SNAKE_CASE     				=  re.sub(r"""layers_(\d+)"""	,r"""layer.\1"""	,snake_case__		)

																		_SCREAMING_SNAKE_CASE     				=  flax_dict[key]

						_SCREAMING_SNAKE_CASE     				=  {}
						# convert converted_dict into torch format
						for key in converted_dict.keys():
												if ("embed_tokens" not in key) and ("embedder" not in key):
																		_SCREAMING_SNAKE_CASE     				=  torch.from_numpy(converted_dict[key].T		)
												else:
																		_SCREAMING_SNAKE_CASE     				=  torch.from_numpy(converted_dict[key]		)

						return converted_torch_dict

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__=False	,snake_case__=False		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  get_flax_param(snake_case__		)

						if not use_large:
												_SCREAMING_SNAKE_CASE     				=  PixaStructVisionConfig()
												_SCREAMING_SNAKE_CASE     				=  PixaStructTextConfig()
						else:
												_SCREAMING_SNAKE_CASE     				=  PixaStructVisionConfig(
												    hidden_size=15_36	,d_ff=39_68	,num_attention_heads=24	,num_hidden_layers=18		)
												_SCREAMING_SNAKE_CASE     				=  PixaStructTextConfig(hidden_size=15_36	,d_ff=39_68	,num_heads=24	,num_layers=18		)
						_SCREAMING_SNAKE_CASE     				=  PixaStructConfig(
						    vision_config=encoder_config.to_dict()	,text_config=decoder_config.to_dict()	,is_vqa=snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  PixaStructForConditionalGeneration(snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  rename_and_convert_flax_params(snake_case__		)
						model.load_state_dict(snake_case__		)

						_SCREAMING_SNAKE_CASE     				=  AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""		)
						_SCREAMING_SNAKE_CASE     				=  PixaStructImageProcessor()
						_SCREAMING_SNAKE_CASE     				=  PixaStructProcessor(image_processor=snake_case__	,tokenizer=snake_case__		)

						if use_large:
												_SCREAMING_SNAKE_CASE     				=  40_96

						_SCREAMING_SNAKE_CASE     				=  True

						# mkdir if needed
						os.makedirs(snake_case__	,exist_ok=snake_case__		)

						model.save_pretrained(snake_case__		)
						processor.save_pretrained(snake_case__		)

						print("""Model saved in {}""".format(snake_case__		)		)


if __name__ == "__main__":
		UpperCamelCase			    =     argparse.ArgumentParser()
		parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
		parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
		UpperCamelCase			    =     parser.parse_args()

		convert_pixastruct_original_pytorch_checkpoint_to_hf(
		    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
		)









# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
		from PIL import Image



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Dict									=							"naver-clova-ix/donut-base-finetuned-docvqa"
			__snake_case    :				Dict									=							(
			    "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
			    "should be the document containing the information, as well as a `question` that is the question about the "
			    "document. It returns a text that contains the answer to the question."
			)
			__snake_case    :				Dict									=							"document_qa"
			__snake_case    :				Any									=							AutoProcessor
			__snake_case    :				int									=							VisionEncoderDecoderModel

			__snake_case    :				Union[str, Any]									=							["image", "text"]
			__snake_case    :				Optional[int]									=							["text"]







			def __init__(     self:  List[Any]				,     *UpperCAmelCase_:  Optional[int]				,     **UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									if not is_vision_available():
															raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""					)

									super().__init__(*UpperCAmelCase_				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  "Image"				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
									_SCREAMING_SNAKE_CASE     				=  task_prompt.replace("""{user_input}"""				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.tokenizer(
									    UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_				,     return_tensors="""pt"""					).input_ids
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor(UpperCAmelCase_				,     return_tensors="""pt"""					).pixel_values

									return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									return self.model.generate(
									    inputs["""pixel_values"""].to(self.device					)				,     decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device					)				,     max_length=self.model.decoder.config.max_position_embeddings				,     early_stopping=UpperCAmelCase_				,     pad_token_id=self.pre_processor.tokenizer.pad_token_id				,     eos_token_id=self.pre_processor.tokenizer.eos_token_id				,     use_cache=UpperCAmelCase_				,     num_beams=1				,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]				,     return_dict_in_generate=UpperCAmelCase_				,     ).sequences







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  Tuple					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.batch_decode(UpperCAmelCase_					)[0]
									_SCREAMING_SNAKE_CASE     				=  sequence.replace(self.pre_processor.tokenizer.eos_token				,     """"""					)
									_SCREAMING_SNAKE_CASE     				=  sequence.replace(self.pre_processor.tokenizer.pad_token				,     """"""					)
									_SCREAMING_SNAKE_CASE     				=  re.sub(R"""<.*?>"""				,     """"""				,     UpperCAmelCase_				,     count=1					).strip()  # remove first task start token
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.tokenajson(UpperCAmelCase_					)

									return sequence["answer"]

