






import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
		from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image


class       __lowercase      :







					'''simple docstring'''




					def __init__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			Tuple=13 ,	UpperCamelCase_       :			List[str]=64 ,	UpperCamelCase_       :			int=2 ,	UpperCamelCase_       :			Dict=3 ,	UpperCamelCase_       :			Any=True ,	UpperCamelCase_       :			Dict=True ,	UpperCamelCase_       :			Any=32 ,	UpperCamelCase_       :			Tuple=5 ,	UpperCamelCase_       :			List[Any]=4 ,	UpperCamelCase_       :			List[str]=37 ,	UpperCamelCase_       :			List[Any]="gelu" ,	UpperCamelCase_       :			int=0.1 ,	UpperCamelCase_       :			int=0.1 ,	UpperCamelCase_       :			Optional[int]=10 ,	UpperCamelCase_       :			List[str]=0.02 ,	UpperCamelCase_       :			str=[1, 16, 4, 4] ,	UpperCamelCase_       :			str=None ,	):


									"""simple docstring"""



									__A         =	parent
									__A         =	batch_size
									__A         =	image_size
									__A         =	patch_size
									__A         =	num_channels
									__A         =	is_training
									__A         =	use_labels
									__A         =	hidden_size
									__A         =	num_hidden_layers
									__A         =	num_attention_heads
									__A         =	intermediate_size
									__A         =	hidden_act
									__A         =	hidden_dropout_prob
									__A         =	attention_probs_dropout_prob
									__A         =	type_sequence_label_size
									__A         =	initializer_range
									__A         =	scope
									__A         =	backbone_featmap_shape

									# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									# the number of patches is based on the feature map of the backbone, which by default uses an output stride
									# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
									__A         =	(self.image_size // 32) ** 2
									__A         =	num_patches + 1




					def 		lowerCAmelCase_     (				self       :			List[Any]				):


									"""simple docstring"""



									__A         =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

									__A         =	None
									if self.use_labels:
													__A         =	ids_tensor([self.batch_size] ,	self.type_sequence_label_size				)

									__A         =	self.get_config()

									return config, pixel_values, labels




					def 		lowerCAmelCase_     (				self       :			List[str]				):


									"""simple docstring"""



									__A         =	{
									    """global_padding""": """same""",
									    """layer_type""": """bottleneck""",
									    """depths""": [3, 4, 9],
									    """out_features""": ["""stage1""", """stage2""", """stage3"""],
									    """embedding_dynamic_padding""": True,
									    """hidden_sizes""": [4, 8, 16, 32],
									    """num_groups""": 2,
									}

									return ViTHybridConfig(
									    image_size=self.image_size ,	patch_size=self.patch_size ,	num_channels=self.num_channels ,	hidden_size=self.hidden_size ,	num_hidden_layers=self.num_hidden_layers ,	num_attention_heads=self.num_attention_heads ,	intermediate_size=self.intermediate_size ,	hidden_act=self.hidden_act ,	hidden_dropout_prob=self.hidden_dropout_prob ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob ,	is_decoder=__UpperCamelCase ,	initializer_range=self.initializer_range ,	backbone_featmap_shape=self.backbone_featmap_shape ,	backbone_config=__UpperCamelCase ,	)




					def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Tuple				):


									"""simple docstring"""



									__A         =	ViTHybridModel(config=__UpperCamelCase				)
									model.to(__UpperCamelCase				)
									model.eval()
									__A         =	model(__UpperCamelCase				)
									self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, self.seq_length, self.hidden_size)				)




					def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Dict				):


									"""simple docstring"""



									__A         =	self.type_sequence_label_size
									__A         =	ViTHybridForImageClassification(__UpperCamelCase				)
									model.to(__UpperCamelCase				)
									model.eval()
									__A         =	model(__UpperCamelCase ,	labels=__UpperCamelCase				)
									self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.type_sequence_label_size)				)





					def 		lowerCAmelCase_     (				self       :			int				):


									"""simple docstring"""



									__A         =	self.prepare_config_and_inputs()
									__A    ,	__A    ,	__A         =	config_and_inputs
									__A         =	{"""pixel_values""": pixel_values}
									return config, inputs_dict


@require_torch
class       __lowercase      (	_UpperCAmelCase			,      _UpperCAmelCase			,      unittest.TestCase  ):







					'''simple docstring'''
					SCREAMING_SNAKE_CASE   						=				(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
					SCREAMING_SNAKE_CASE   						=				(
					    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
					    if is_torch_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE   						=				False
					SCREAMING_SNAKE_CASE   						=				False
					SCREAMING_SNAKE_CASE   						=				False




					def 		lowerCAmelCase_     (				self       :			str				):


									"""simple docstring"""



									__A         =	ViTHybridModelTester(self				)
									__A         =	ConfigTester(self ,	config_class=__UpperCamelCase ,	has_text_modality=__UpperCamelCase ,	hidden_size=37				)




					def 		lowerCAmelCase_     (				self       :			Any				):


									"""simple docstring"""



									self.config_tester.run_common_tests()




					@unittest.skip(reason="""ViT does not use inputs_embeds"""				)
					def 		lowerCAmelCase_     (				self       :			List[Any]				):


									"""simple docstring"""



									pass




					def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


									"""simple docstring"""



									__A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													__A         =	model_class(__UpperCamelCase				)
													self.assertIsInstance(model.get_input_embeddings() ,	(nn.Module)				)
													__A         =	model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__UpperCamelCase ,	nn.Linear				)				)




					def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


									"""simple docstring"""



									__A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()

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

													__A         =	["""pixel_values"""]
													self.assertListEqual(arg_names[:1] ,	__UpperCamelCase				)




					def 		lowerCAmelCase_     (				self       :			int				):


									"""simple docstring"""



									__A         =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCamelCase				)




					def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


									"""simple docstring"""



									__A         =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase				)




					def 		lowerCAmelCase_     (				self       :			Any				):


									"""simple docstring"""



									__A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()

									__A         =	_config_zero_init(__UpperCamelCase				)
									for model_class in self.all_model_classes:
													__A         =	model_class(config=__UpperCamelCase				)
													# Skip the check for the backbone
													for name, module in model.named_modules():
																	if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																					__A         =	[F"{name}.{key}" for key in module.state_dict().keys()]
																					break

													for name, param in model.named_parameters():
																	if param.requires_grad:
																					if name in backbone_params:
																									continue
																					self.assertIn(
																					    ((param.data.mean() * 1e9).round() / 1e9).item() ,	[0.0, 1.0] ,	msg=F"Parameter {name} of model {model_class} seems not properly initialized" ,	)





					@slow
					def 		lowerCAmelCase_     (				self       :			Tuple				):


									"""simple docstring"""



									for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__A         =	ViTHybridModel.from_pretrained(__UpperCamelCase				)
													self.assertIsNotNone(__UpperCamelCase				)



def 			_SCREAMING_SNAKE_CASE						( )						->      Optional[int]:





				"""simple docstring"""

				__A         =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
				return image






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







					'''simple docstring'''




					@cached_property
					def 		lowerCAmelCase_     (				self       :			str				):


									"""simple docstring"""



									return (
									    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
									    if is_vision_available()
									    else None
									)




					@slow
					def 		lowerCAmelCase_     (				self       :			List[Any]				):


									"""simple docstring"""



									__A         =	ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(
									    __UpperCamelCase				)

									__A         =	self.default_image_processor
									__A         =	prepare_img()
									__A         =	image_processor(images=__UpperCamelCase ,	return_tensors="""pt"""				).to(__UpperCamelCase				)

									# forward pass
									with torch.no_grad():
													__A         =	model(**__UpperCamelCase				)

									# verify the logits
									__A         =	torch.Size((1, 1_000)				)
									self.assertEqual(outputs.logits.shape ,	__UpperCamelCase				)

									__A         =	torch.tensor([-1.9090, -0.4993, -0.2389]				).to(__UpperCamelCase				)

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





					@slow
					@require_accelerate
					def 		lowerCAmelCase_     (				self       :			List[Any]				):


									"""simple docstring"""



									__A         =	ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""				)
									__A         =	ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384""" ,	device_map="""auto"""				)

									__A         =	prepare_img()

									__A         =	image_processor(images=__UpperCamelCase ,	return_tensors="""pt"""				)
									__A         =	model(**__UpperCamelCase				)
									__A         =	outputs.logits
									# model predicts one of the 1000 ImageNet classes
									__A         =	logits.argmax(-1				).item()

									self.assertTrue(model.config.idalabel[predicted_class_idx] ,	"""tabby, tabby cat"""				)












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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int    )						->      Union[str, Any]:





    """simple docstring"""

    __A         =	checkpoints.load_tax_checkpoint(__lowercase    )
    __A         =	flatten_dict(__lowercase    )
    return flax_params



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]    )						->      List[str]:





    """simple docstring"""

    __A         =	{}

    __A         =	{
        """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""",
    }

    __A         =	{
        """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
            __A         =	""".""".join(key[1:]    )

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

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

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

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

            __A         =	flax_dict[key]

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

    return converted_torch_dict



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			List[Any]			,				__lowercase   :			Dict=False			,				__lowercase   :			List[str]=False    )						->      Any:





    """simple docstring"""

    __A         =	get_flax_param(__lowercase    )

    if not use_large:
        __A         =	PixaStructVisionConfig()
        __A         =	PixaStructTextConfig()
    else:
        __A         =	PixaStructVisionConfig(
            hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_attention_heads=2_4			,				num_hidden_layers=1_8    )
        __A         =	PixaStructTextConfig(hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_heads=2_4			,				num_layers=1_8    )
    __A         =	PixaStructConfig(
        vision_config=encoder_config.to_dict()			,				text_config=decoder_config.to_dict()			,				is_vqa=__lowercase    )

    __A         =	PixaStructForConditionalGeneration(__lowercase    )

    __A         =	rename_and_convert_flax_params(__lowercase    )
    model.load_state_dict(__lowercase    )

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

    if use_large:
        __A         =	4_0_9_6

    __A         =	True

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

    model.save_pretrained(__lowercase    )
    processor.save_pretrained(__lowercase    )

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


if __name__ == "__main__":
  __a      :       Union[str, Any]				  =   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      :       Dict				  =   parser.parse_args()

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




