






from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
		from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor


class       __lowercase      (	lowercase_  ):







					'''simple docstring'''




					def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


									"""simple docstring"""



									__A         =	self.config_class(**self.inputs_dict				)
									self.parent.assertTrue(hasattr(UpperCamelCase_ ,	"""embed_dim"""				)				)
									self.parent.assertTrue(hasattr(UpperCamelCase_ ,	"""num_heads"""				)				)


class       __lowercase      :







					'''simple docstring'''




					def __init__(				self       :			Tuple ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Dict=13 ,	UpperCamelCase_       :			int=64 ,	UpperCamelCase_       :			Dict=3 ,	UpperCamelCase_       :			List[str]=[16, 48, 96] ,	UpperCamelCase_       :			Tuple=[1, 3, 6] ,	UpperCamelCase_       :			Optional[int]=[1, 2, 10] ,	UpperCamelCase_       :			List[str]=[7, 3, 3] ,	UpperCamelCase_       :			Any=[4, 2, 2] ,	UpperCamelCase_       :			str=[2, 1, 1] ,	UpperCamelCase_       :			List[str]=[2, 2, 2] ,	UpperCamelCase_       :			int=[False, False, True] ,	UpperCamelCase_       :			List[Any]=[0.0, 0.0, 0.0] ,	UpperCamelCase_       :			Tuple=0.02 ,	UpperCamelCase_       :			Dict=1e-12 ,	UpperCamelCase_       :			Any=True ,	UpperCamelCase_       :			int=True ,	UpperCamelCase_       :			str=2 ,	):


									"""simple docstring"""



									__A         =	parent
									__A         =	batch_size
									__A         =	image_size
									__A         =	patch_sizes
									__A         =	patch_stride
									__A         =	patch_padding
									__A         =	is_training
									__A         =	use_labels
									__A         =	num_labels
									__A         =	num_channels
									__A         =	embed_dim
									__A         =	num_heads
									__A         =	stride_kv
									__A         =	depth
									__A         =	cls_token
									__A         =	attention_drop_rate
									__A         =	initializer_range
									__A         =	layer_norm_eps




					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:
													# create a random int32 tensor of given shape
													__A         =	ids_tensor([self.batch_size] ,	self.num_labels				)

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




					def 		lowerCAmelCase_     (				self       :			Tuple				):


									"""simple docstring"""



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


									"""simple docstring"""



									__A         =	TFCvtModel(config=UpperCamelCase_				)
									__A         =	model(UpperCamelCase_ ,	training=UpperCamelCase_				)
									__A         =	(self.image_size, self.image_size)
									__A    ,	__A         =	image_size[0], image_size[1]
									for i in range(len(self.depth				)				):
													__A         =	floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1				)
													__A         =	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 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Union[str, Any]				):


									"""simple docstring"""



									__A         =	self.num_labels
									__A         =	TFCvtForImageClassification(UpperCamelCase_				)
									__A         =	model(UpperCamelCase_ ,	labels=UpperCamelCase_ ,	training=UpperCamelCase_				)
									self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.num_labels)				)





					def 		lowerCAmelCase_     (				self       :			List[str]				):


									"""simple docstring"""



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


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







					'''simple docstring'''
					SCREAMING_SNAKE_CASE   						=				(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE   						=				(
					    {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
					    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       :			List[Any]				):


									"""simple docstring"""



									__A         =	TFCvtModelTester(self				)
									__A         =	TFCvtConfigTester(self ,	config_class=UpperCamelCase_ ,	has_text_modality=UpperCamelCase_ ,	hidden_size=37				)




					def 		lowerCAmelCase_     (				self       :			List[Any]				):


									"""simple docstring"""



									self.config_tester.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()




					@unittest.skip(reason="""Cvt does not output attentions"""				)
					def 		lowerCAmelCase_     (				self       :			Tuple				):


									"""simple docstring"""



									pass




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


									"""simple docstring"""



									pass




					@unittest.skip(reason="""Cvt does not support input and output embeddings"""				)
					def 		lowerCAmelCase_     (				self       :			Any				):


									"""simple docstring"""



									pass




					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""				)				) == 0 ,	reason="""TF does not support backprop for grouped convolutions on CPU.""" ,	)
					def 		lowerCAmelCase_     (				self       :			List[str]				):


									"""simple docstring"""



									super().test_dataset_conversion()




					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""				)				) == 0 ,	reason="""TF does not support backprop for grouped convolutions on CPU.""" ,	)
					@slow
					def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


									"""simple docstring"""



									super().test_keras_fit()




					@unittest.skip(reason="""Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8"""				)
					def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


									"""simple docstring"""



									__A         =	tf.keras.mixed_precision.Policy("""mixed_float16"""				)
									tf.keras.mixed_precision.set_global_policy(UpperCamelCase_				)
									super().test_keras_fit()
									tf.keras.mixed_precision.set_global_policy("""float32"""				)




					def 		lowerCAmelCase_     (				self       :			List[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.call				)
													# 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"""



									def check_hidden_states_output(UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Optional[int]				):
													__A         =	model_class(UpperCamelCase_				)

													__A         =	model(**self._prepare_for_class(UpperCamelCase_ ,	UpperCamelCase_				)				)
													__A         =	outputs.hidden_states

													__A         =	len(self.model_tester.depth				)
													self.assertEqual(len(UpperCamelCase_				) ,	UpperCamelCase_				)

													# 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,
													    ] ,	)

									__A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													__A         =	True
													check_hidden_states_output(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)

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

													check_hidden_states_output(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)




					def 		lowerCAmelCase_     (				self       :			List[str]				):


									"""simple docstring"""



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




					def 		lowerCAmelCase_     (				self       :			Optional[int]				):


									"""simple docstring"""



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





					@slow
					def 		lowerCAmelCase_     (				self       :			Dict				):


									"""simple docstring"""



									for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__A         =	TFCvtModel.from_pretrained(UpperCamelCase_				)
													self.assertIsNotNone(UpperCamelCase_				)



def 			_SCREAMING_SNAKE_CASE						( )						->      str:





				"""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       :			int				):


									"""simple docstring"""



									return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)





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


									"""simple docstring"""



									__A         =	TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

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

									# forward pass
									__A         =	model(**UpperCamelCase_				)

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

									__A         =	tf.constant([0.9285, 0.9015, -0.3150]				)
									self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() ,	UpperCamelCase_ ,	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
  )




