

import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import CvtForImageClassification, CvtModel
	from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor



class   lowercase_   (  __lowercase					):



						def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Union[str, Any]:
								_snake_case						=							self.config_class(**self.inputs_dict		)
								self.parent.assertTrue(hasattr(A__      , '''embed_dim'''		)		)
								self.parent.assertTrue(hasattr(A__      , '''num_heads'''		)		)



class   lowercase_   :



						def __init__(      self				:    str      , A__				:    Any      , A__				:    List[Any]=13      , A__				:    Optional[Any]=64      , A__				:    Union[str, Any]=3      , A__				:    Optional[Any]=[16, 48, 96]      , A__				:    List[str]=[1, 3, 6]      , A__				:    Union[str, Any]=[1, 2, 10]      , A__				:    Dict=[7, 3, 3]      , A__				:    Dict=[4, 2, 2]      , A__				:    List[Any]=[2, 1, 1]      , A__				:    Dict=[2, 2, 2]      , A__				:    Union[str, Any]=[False, False, True]      , A__				:    List[Any]=[0.0, 0.0, 0.0]      , A__				:    List[str]=0.02      , A__				:    Tuple=1e-12      , A__				:    Tuple=True      , A__				:    List[Any]=True      , A__				:    Optional[int]=2      , )   ->					str:
								_snake_case						=							parent
								_snake_case						=							batch_size
								_snake_case						=							image_size
								_snake_case						=							patch_sizes
								_snake_case						=							patch_stride
								_snake_case						=							patch_padding
								_snake_case						=							is_training
								_snake_case						=							use_labels
								_snake_case						=							num_labels
								_snake_case						=							num_channels
								_snake_case						=							embed_dim
								_snake_case						=							num_heads
								_snake_case						=							stride_kv
								_snake_case						=							depth
								_snake_case						=							cls_token
								_snake_case						=							attention_drop_rate
								_snake_case						=							initializer_range
								_snake_case						=							layer_norm_eps



						def 						UpperCamelCase_		(      self				:    Tuple		)   ->					int:
								_snake_case						=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

								_snake_case						=							None
								if self.use_labels:
										_snake_case						=							ids_tensor([self.batch_size]      , self.num_labels		)

								_snake_case						=							self.get_config()
								return config, pixel_values, labels



						def 						UpperCamelCase_		(      self				:    int		)   ->					Optional[Any]:
								return CvtConfig(
								    image_size=self.image_size      , num_labels=self.num_labels      , num_channels=self.num_channels      , embed_dim=self.embed_dim      , num_heads=self.num_heads      , patch_sizes=self.patch_sizes      , patch_padding=self.patch_padding      , patch_stride=self.patch_stride      , stride_kv=self.stride_kv      , depth=self.depth      , cls_token=self.cls_token      , attention_drop_rate=self.attention_drop_rate      , initializer_range=self.initializer_range      , )



						def 						UpperCamelCase_		(      self				:    Dict      , A__				:    Dict      , A__				:    Union[str, Any]      , A__				:    Union[str, Any]		)   ->					int:
								_snake_case						=							CvtModel(config=A__		)
								model.to(A__		)
								model.eval()
								_snake_case						=							model(A__		)
								_snake_case						=							(self.image_size, self.image_size)
								_snake_case,					_snake_case						=							image_size[0], image_size[1]
								for i in range(len(self.depth		)		):
										_snake_case						=							floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1		)
										_snake_case						=							floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1		)
								self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.embed_dim[-1], height, width)		)



						def 						UpperCamelCase_		(      self				:    str      , A__				:    Optional[int]      , A__				:    Optional[Any]      , A__				:    str		)   ->					Union[str, Any]:
								_snake_case						=							self.num_labels
								_snake_case						=							CvtForImageClassification(A__		)
								model.to(A__		)
								model.eval()
								_snake_case						=							model(A__      , labels=A__		)
								self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)		)







						def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Optional[Any]:
								_snake_case						=							self.prepare_config_and_inputs()
								_snake_case,					_snake_case,					_snake_case						=							config_and_inputs
								_snake_case						=							{'''pixel_values''': pixel_values}
								return config, inputs_dict



@require_torch
class   lowercase_   (  __lowercase       ,							__lowercase       ,							unittest.TestCase					):
						UpperCamelCase_      :		int										=    (CvtModel, CvtForImageClassification) if is_torch_available() else ()
						UpperCamelCase_      :		List[str]										=    (
						    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase_      :		Any										=    False
						UpperCamelCase_      :		Union[str, Any]										=    False
						UpperCamelCase_      :		Any										=    False
						UpperCamelCase_      :		List[str]										=    False
						UpperCamelCase_      :		Optional[int]										=    False



						def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Any:
								_snake_case						=							CvtModelTester(self		)
								_snake_case						=							ConfigTester(self      , config_class=A__      , has_text_modality=A__      , hidden_size=37		)



						def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Tuple:
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()



						def 						UpperCamelCase_		(      self				:    Dict		)   ->					str:
								return



						@unittest.skip(reason='''Cvt does not output attentions'''		)
						def 						UpperCamelCase_		(      self				:    Dict		)   ->					int:
								pass



						@unittest.skip(reason='''Cvt does not use inputs_embeds'''		)
						def 						UpperCamelCase_		(      self				:    int		)   ->					Optional[Any]:
								pass



						@unittest.skip(reason='''Cvt does not support input and output embeddings'''		)
						def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Optional[int]:
								pass



						def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Union[str, Any]:
								_snake_case,					_snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										_snake_case						=							model_class(A__		)
										_snake_case						=							inspect.signature(model.forward		)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										_snake_case						=							[*signature.parameters.keys()]

										_snake_case						=							['''pixel_values''']
										self.assertListEqual(arg_names[:1]      , A__		)



						def 						UpperCamelCase_		(      self				:    str		)   ->					Dict:
								_snake_case						=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A__		)



						def 						UpperCamelCase_		(      self				:    str		)   ->					Optional[int]:
								def check_hidden_states_output(A__				:    Tuple      , A__				:    int      , A__				:    Any		):
										_snake_case						=							model_class(A__		)
										model.to(A__		)
										model.eval()

										with torch.no_grad():
												_snake_case						=							model(**self._prepare_for_class(A__      , A__		)		)

										_snake_case						=							outputs.hidden_states

										_snake_case						=							len(self.model_tester.depth		)
										self.assertEqual(len(A__		)      , A__		)

										# verify the first hidden states (first block)
										self.assertListEqual(
										    list(hidden_states[0].shape[-3:]		)      , [
										        self.model_tester.embed_dim[0],
										        self.model_tester.image_size // 4,
										        self.model_tester.image_size // 4,
										    ]      , )

								_snake_case,					_snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										_snake_case						=							True
										check_hidden_states_output(A__      , A__      , A__		)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										_snake_case						=							True

										check_hidden_states_output(A__      , A__      , A__		)



						def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Tuple:
								_snake_case						=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*A__		)



						@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
						def 						UpperCamelCase_		(      self				:    Dict		)   ->					int:
								pass







						@slow
						def 						UpperCamelCase_		(      self				:    Dict		)   ->					Optional[int]:
								for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_snake_case						=							CvtModel.from_pretrained(A__		)
										self.assertIsNotNone(A__		)



def 				snake_case_()							->     List[str]:



		"""simple docstring"""






		_snake_case						=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
		return image



@require_torch
@require_vision
class   lowercase_   (  unittest.TestCase					):



						@cached_property
						def 						UpperCamelCase_		(      self				:    List[str]		)   ->					List[Any]:
								return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)







						@slow
						def 						UpperCamelCase_		(      self				:    str		)   ->					str:
								_snake_case						=							CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(A__		)

								_snake_case						=							self.default_image_processor
								_snake_case						=							prepare_img()
								_snake_case						=							image_processor(images=A__      , return_tensors='''pt'''		).to(A__		)

								# forward pass
								with torch.no_grad():
										_snake_case						=							model(**A__		)

								# verify the logits
								_snake_case						=							torch.Size((1, 1000)		)
								self.assertEqual(outputs.logits.shape      , A__		)

								_snake_case						=							torch.tensor([0.9285, 0.9015, -0.3150]		).to(A__		)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]      , A__      , atol=1e-4		)		)




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 				snake_case_(_UpperCamelCase					)							->     Optional[int]:



  """simple docstring"""






  _snake_case						=							checkpoints.load_tax_checkpoint(_UpperCamelCase					)
  _snake_case						=							flatten_dict(_UpperCamelCase					)
  return flax_params



def 				snake_case_(_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  _snake_case						=							{}

  _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''',
  }

  _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
      _snake_case						=							'''.'''.join(key[1:]					)

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

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

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        _snake_case						=							re.sub(R'''layers_(\d+)'''			,	R'''layer.\1'''			,	_UpperCamelCase					)
        _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
        _snake_case						=							re.sub(R'''layers_(\d+)'''			,	R'''layer.\1'''			,	_UpperCamelCase					)

      _snake_case						=							flax_dict[key]

  _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):
      _snake_case						=							torch.from_numpy(converted_dict[key].T					)
    else:
      _snake_case						=							torch.from_numpy(converted_dict[key]					)

  return converted_torch_dict



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=False			,	_UpperCamelCase=False					)							->     List[Any]:



  """simple docstring"""






  _snake_case						=							get_flax_param(_UpperCamelCase					)

  if not use_large:
    _snake_case						=							PixaStructVisionConfig()
    _snake_case						=							PixaStructTextConfig()
  else:
    _snake_case						=							PixaStructVisionConfig(
        hidden_size=1_536			,	d_ff=3_968			,	num_attention_heads=24			,	num_hidden_layers=18					)
    _snake_case						=							PixaStructTextConfig(hidden_size=1_536			,	d_ff=3_968			,	num_heads=24			,	num_layers=18					)
  _snake_case						=							PixaStructConfig(
      vision_config=encoder_config.to_dict()			,	text_config=decoder_config.to_dict()			,	is_vqa=_UpperCamelCase					)

  _snake_case						=							PixaStructForConditionalGeneration(_UpperCamelCase					)

  _snake_case						=							rename_and_convert_flax_params(_UpperCamelCase					)
  model.load_state_dict(_UpperCamelCase					)

  _snake_case						=							AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''					)
  _snake_case						=							PixaStructImageProcessor()
  _snake_case						=							PixaStructProcessor(image_processor=_UpperCamelCase			,	tokenizer=_UpperCamelCase					)

  if use_large:
    _snake_case						=							4_096

  _snake_case						=							True

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

  model.save_pretrained(_UpperCamelCase					)
  processor.save_pretrained(_UpperCamelCase					)

  print('''Model saved in {}'''.format(_UpperCamelCase					)					)


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
 )

