

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__ (					_a):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       checkpoints.load_tax_checkpoint(_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       flatten_dict(_a)
	return flax_params

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      List[str]				     =       {}

	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {
	    "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   :      List[str]				     =       {
	    "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   :      Union[str, Any]				     =       ".".join(key[1:])

			# rename the key
			for old, new in CONVERSION_MAPPING.items():
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       new_key.replace(_a							,			_a)

			if "decoder" in new_key:
				for old, new in DECODER_CONVERSION_MAPPING.items():
					SCREAMING_SNAKE_CASE   :      str				     =       new_key.replace(_a							,			_a)

			if "layers" in new_key and "decoder" not in new_key:
				# use regex to replace the layer number
				SCREAMING_SNAKE_CASE   :      Tuple				     =       re.sub(r"layers_(\d+)"							,			r"layer.\1"							,			_a)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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   :      int				     =       re.sub(r"layers_(\d+)"							,			r"layer.\1"							,			_a)

			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       flax_dict[key]

	SCREAMING_SNAKE_CASE   :      Tuple				     =       {}
	# 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   :      Optional[int]				     =       torch.from_numpy(converted_dict[key].T)
		else:
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.from_numpy(converted_dict[key])

	return converted_torch_dict

def  lowerCamelCase__ (					_a							,			_a							,			_a=False							,			_a=False):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       get_flax_param(_a)

	if not use_large:
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       PixaStructVisionConfig()
		SCREAMING_SNAKE_CASE   :      Tuple				     =       PixaStructTextConfig()
	else:
		SCREAMING_SNAKE_CASE   :      int				     =       PixaStructVisionConfig(
		    hidden_size=1536							,			d_ff=3968							,			num_attention_heads=24							,			num_hidden_layers=18)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       PixaStructTextConfig(hidden_size=1536							,			d_ff=3968							,			num_heads=24							,			num_layers=18)
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       PixaStructConfig(
	    vision_config=encoder_config.to_dict()							,			text_config=decoder_config.to_dict()							,			is_vqa=_a)

	SCREAMING_SNAKE_CASE   :      List[str]				     =       PixaStructForConditionalGeneration(_a)

	SCREAMING_SNAKE_CASE   :      str				     =       rename_and_convert_flax_params(_a)
	model.load_state_dict(_a)

	SCREAMING_SNAKE_CASE   :      List[str]				     =       AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer")
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       PixaStructImageProcessor()
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       PixaStructProcessor(image_processor=_a							,			tokenizer=_a)

	if use_large:
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       4096

	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       True

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

	model.save_pretrained(_a)
	processor.save_pretrained(_a)

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


if __name__ == "__main__":
			a_						     =    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.')
			a_						     =    parser.parse_args()

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


import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image

			from transformers.image_utils import load_image
else:






			class _UpperCamelCase							:
						'''simple docstring'''







						@staticmethod
						def 							__UpperCamelCase (			*a							: Any      ,      **a							: Optional[int]	)							->       Optional[Any]:

							"""simple docstring"""

							pass

			def  lowerCamelCase__ (					_a):
				return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
a_						     =    (
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)






@is_pipeline_test
@require_torch
@require_vision
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
			@require_pytesseract
			@require_vision
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: Union[str, Any]      ,      a							: Any      ,      a							: Optional[Any]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       pipeline(
				    "document-question-answering"      ,      model=a      ,      tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      int				     =       list(zip(*apply_tesseract(load_image(a	)      ,      a      ,      ""	)	)	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "What is the placebo?"
				SCREAMING_SNAKE_CASE   :      List[str]				     =       [
				    {
				        "image": load_image(a	),
				        "question": question,
				    },
				    {
				        "image": image,
				        "question": question,
				    },
				    {
				        "image": image,
				        "question": question,
				        "word_boxes": word_boxes,
				    },
				]
				return dqa_pipeline, examples
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: List[Any]      ,      a							: Any	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       dqa_pipeline(a      ,      top_k=2	)
				self.assertEqual(
				    a      ,      [
				        [
				            {"score": ANY(a	), "answer": ANY(a	), "start": ANY(a	), "end": ANY(a	)},
				            {"score": ANY(a	), "answer": ANY(a	), "start": ANY(a	), "end": ANY(a	)},
				        ]
				    ]
				    * 3      ,      )
			@require_torch
			@require_detectrona
			@require_pytesseract
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       pipeline("document-question-answering"      ,      model="hf-internal-testing/tiny-random-layoutlmv2"	)
				SCREAMING_SNAKE_CASE   :      int				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      int				     =       "How many cats are there?"

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [
				    {"score": 0.0001, "answer": "oy 2312/2019", "start": 38, "end": 39},
				    {"score": 0.0001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
				]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(nested_simplify(a      ,      decimals=4	)      ,      a	)

				SCREAMING_SNAKE_CASE   :      Any				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(nested_simplify(a      ,      decimals=4	)      ,      a	)

				# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
				# Empty answer probably
				SCREAMING_SNAKE_CASE   :      Tuple				     =       "./tests/fixtures/tests_samples/COCO/000000039769.png"
				SCREAMING_SNAKE_CASE   :      str				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(a      ,      []	)

				# We can optionnally pass directly the words and bounding boxes
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "./tests/fixtures/tests_samples/COCO/000000039769.png"
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       []
				SCREAMING_SNAKE_CASE   :      int				     =       []
				SCREAMING_SNAKE_CASE   :      str				     =       dqa_pipeline(image=a      ,      question=a      ,      words=a      ,      boxes=a      ,      top_k=2	)
				self.assertEqual(a      ,      []	)
			@slow
			@require_torch
			@require_detectrona
			@require_pytesseract
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       pipeline(
				    "document-question-answering"      ,      model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"      ,      revision="9977165"      ,      )
				SCREAMING_SNAKE_CASE   :      Any				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      Any				     =       "What is the invoice number?"

				SCREAMING_SNAKE_CASE   :      Dict				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Any				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        [
				            {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
				            {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
				        ],
				    ]
				    * 2      ,      )
			@slow
			@require_torch
			@require_detectrona
			@require_pytesseract
			def 							__UpperCamelCase (			self							: Tuple	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       pipeline(
				    "document-question-answering"      ,      model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"      ,      revision="9977165"      ,      max_seq_len=50      ,      )
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      int				     =       "What is the invoice number?"

				SCREAMING_SNAKE_CASE   :      Any				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
				        {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Tuple				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
				        {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        [
				            {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
				            {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
				        ]
				    ]
				    * 2      ,      )
			@slow
			@require_torch
			@require_pytesseract
			@require_vision
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       AutoTokenizer.from_pretrained(
				    "impira/layoutlm-document-qa"      ,      revision="3dc6de3"      ,      add_prefix_space=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       pipeline(
				    "document-question-answering"      ,      model="impira/layoutlm-document-qa"      ,      tokenizer=a      ,      revision="3dc6de3"      ,      )
				SCREAMING_SNAKE_CASE   :      List[str]				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      int				     =       "What is the invoice number?"

				SCREAMING_SNAKE_CASE   :      str				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Dict				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        [
				            {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
				            {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
				        ]
				    ]
				    * 2      ,      )

				SCREAMING_SNAKE_CASE   :      int				     =       list(zip(*apply_tesseract(load_image(a	)      ,      a      ,      ""	)	)	)

				# This model should also work if `image` is set to None
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
				    ]      ,      )
			@slow
			@require_torch
			@require_pytesseract
			@require_vision
			def 							__UpperCamelCase (			self							: Tuple	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       AutoTokenizer.from_pretrained(
				    "impira/layoutlm-document-qa"      ,      revision="3dc6de3"      ,      add_prefix_space=a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       pipeline(
				    "document-question-answering"      ,      model="impira/layoutlm-document-qa"      ,      tokenizer=a      ,      revision="3dc6de3"      ,      max_seq_len=50      ,      )
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "What is the invoice number?"

				SCREAMING_SNAKE_CASE   :      Dict				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      int				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        [
				            {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
				            {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
				        ]
				    ]
				    * 2      ,      )

				SCREAMING_SNAKE_CASE   :      int				     =       list(zip(*apply_tesseract(load_image(a	)      ,      a      ,      ""	)	)	)

				# This model should also work if `image` is set to None
				SCREAMING_SNAKE_CASE   :      Any				     =       dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )
			@slow
			@require_torch
			def 							__UpperCamelCase (			self							: str	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       pipeline(
				    "document-question-answering"      ,      model="naver-clova-ix/donut-base-finetuned-docvqa"      ,      tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa"	)      ,      feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa"      ,      )

				SCREAMING_SNAKE_CASE   :      int				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "What is the invoice number?"
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(nested_simplify(a      ,      decimals=4	)      ,      [{"answer": "us-001"}]	)






			@require_tf
			@unittest.skip("Document question answering not implemented in TF"	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       int:

				"""simple docstring"""

				pass