






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFResNetForImageClassification, TFResNetModel
		from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor


class       __lowercase      :







					'''simple docstring'''




					def __init__(				self       :			Tuple ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Tuple=3 ,	UpperCamelCase_       :			str=32 ,	UpperCamelCase_       :			Dict=3 ,	UpperCamelCase_       :			List[str]=10 ,	UpperCamelCase_       :			str=[10, 20, 30, 40] ,	UpperCamelCase_       :			Tuple=[1, 1, 2, 1] ,	UpperCamelCase_       :			Dict=True ,	UpperCamelCase_       :			Optional[Any]=True ,	UpperCamelCase_       :			Optional[int]="relu" ,	UpperCamelCase_       :			Optional[int]=3 ,	UpperCamelCase_       :			int=None ,	):


									"""simple docstring"""



									__A         =	parent
									__A         =	batch_size
									__A         =	image_size
									__A         =	num_channels
									__A         =	embeddings_size
									__A         =	hidden_sizes
									__A         =	depths
									__A         =	is_training
									__A         =	use_labels
									__A         =	hidden_act
									__A         =	num_labels
									__A         =	scope
									__A         =	len(_lowercase				)




					def 		lowerCAmelCase_     (				self       :			Union[str, 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.num_labels				)

									__A         =	self.get_config()

									return config, pixel_values, labels




					def 		lowerCAmelCase_     (				self       :			Dict				):


									"""simple docstring"""



									return ResNetConfig(
									    num_channels=self.num_channels ,	embeddings_size=self.embeddings_size ,	hidden_sizes=self.hidden_sizes ,	depths=self.depths ,	hidden_act=self.hidden_act ,	num_labels=self.num_labels ,	image_size=self.image_size ,	)




					def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			str				):


									"""simple docstring"""



									__A         =	TFResNetModel(config=_lowercase				)
									__A         =	model(_lowercase				)
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape ,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,	)




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


									"""simple docstring"""



									__A         =	self.num_labels
									__A         =	TFResNetForImageClassification(_lowercase				)
									__A         =	model(_lowercase ,	labels=_lowercase				)
									self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.num_labels)				)





					def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


									"""simple docstring"""



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


@require_tf
class       __lowercase      (	__snake_case			,      __snake_case			,      unittest.TestCase  ):







					'''simple docstring'''
					SCREAMING_SNAKE_CASE   						=				(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE   						=				(
					    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
					    if is_tf_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE   						=				False
					SCREAMING_SNAKE_CASE   						=				False
					SCREAMING_SNAKE_CASE   						=				False
					SCREAMING_SNAKE_CASE   						=				False
					SCREAMING_SNAKE_CASE   						=				False




					def 		lowerCAmelCase_     (				self       :			Any				):


									"""simple docstring"""



									__A         =	TFResNetModelTester(self				)
									__A         =	ConfigTester(self ,	config_class=_lowercase ,	has_text_modality=_lowercase				)




					def 		lowerCAmelCase_     (				self       :			Tuple				):


									"""simple docstring"""



									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 		lowerCAmelCase_     (				self       :			Tuple				):


									"""simple docstring"""



									return




					@unittest.skip(reason="""ResNet does not use inputs_embeds"""				)
					def 		lowerCAmelCase_     (				self       :			str				):


									"""simple docstring"""



									pass




					@unittest.skip(reason="""ResNet does not support input and output embeddings"""				)
					def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


									"""simple docstring"""



									pass




					def 		lowerCAmelCase_     (				self       :			Tuple				):


									"""simple docstring"""



									__A         =	self.model_tester.prepare_config_and_inputs_for_common()

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

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




					def 		lowerCAmelCase_     (				self       :			str				):


									"""simple docstring"""



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




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


									"""simple docstring"""



									def check_hidden_states_output(UpperCamelCase_       :			str ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Any				):
													__A         =	model_class(_lowercase				)
													__A         =	model(**self._prepare_for_class(_lowercase ,	_lowercase				)				)

													__A         =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													__A         =	self.model_tester.num_stages
													self.assertEqual(len(_lowercase				) ,	expected_num_stages + 1				)

													# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]				) ,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,	)

									__A         =	self.model_tester.prepare_config_and_inputs_for_common()
									__A         =	["""basic""", """bottleneck"""]
									for model_class in self.all_model_classes:
													for layer_type in layers_type:
																	__A         =	layer_type
																	__A         =	True
																	check_hidden_states_output(_lowercase ,	_lowercase ,	_lowercase				)

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

																	check_hidden_states_output(_lowercase ,	_lowercase ,	_lowercase				)




					def 		lowerCAmelCase_     (				self       :			int				):


									"""simple docstring"""



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





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


									"""simple docstring"""



									for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__A         =	TFResNetModel.from_pretrained(_lowercase				)
													self.assertIsNotNone(_lowercase				)



def 			_SCREAMING_SNAKE_CASE						( )						->      int:





				"""simple docstring"""

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






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







					'''simple docstring'''




					@cached_property
					def 		lowerCAmelCase_     (				self       :			Optional[int]				):


									"""simple docstring"""



									return (
									    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
									    if is_vision_available()
									    else None
									)





					@slow
					def 		lowerCAmelCase_     (				self       :			Optional[int]				):


									"""simple docstring"""



									__A         =	TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

									__A         =	self.default_image_processor
									__A         =	prepare_img()
									__A         =	image_processor(images=_lowercase ,	return_tensors="""tf"""				)

									# forward pass
									__A         =	model(**_lowercase				)

									# verify the logits
									__A         =	tf.TensorShape((1, 1_000)				)
									self.assertEqual(outputs.logits.shape ,	_lowercase				)

									__A         =	tf.constant([-11.1069, -9.7877, -8.3777]				)

									self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() ,	_lowercase ,	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 			_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
  )




