




import inspect
import unittest

from transformers import RegNetConfig
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 torch import nn

			from transformers import RegNetForImageClassification, RegNetModel
			from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor







class a :
						"""simple docstring"""

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


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






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Any:
													"""simple docstring"""


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

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

													__lowercase      =    self.get_config()

													return config, pixel_values, labels






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						int:
													"""simple docstring"""


													return RegNetConfig(
													    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				,							)






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Any				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    RegNetModel(config=lowerCamelCase__				)
													model.to(lowerCamelCase__				)
													model.eval()
													__lowercase      =    model(lowerCamelCase__				)
													# 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     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     Optional[int]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.num_labels
													__lowercase      =    RegNetForImageClassification(lowerCamelCase__				)
													model.to(lowerCamelCase__				)
													model.eval()
													__lowercase      =    model(lowerCamelCase__				,							labels=lowerCamelCase__				)
													self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.prepare_config_and_inputs()
													__lowercase     ,     __lowercase     ,     __lowercase      =    config_and_inputs
													__lowercase      =    {'''pixel_values''': pixel_values}
													return config, inputs_dict







@require_torch
class a (     __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       Optional[Any]       	=		(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
						UpperCamelCase_	:       int       	=		(
						    {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase_	:       Tuple       	=		False
						UpperCamelCase_	:       str       	=		False
						UpperCamelCase_	:       Dict       	=		False
						UpperCamelCase_	:       List[str]       	=		False






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    RegNetModelTester(self				)
													__lowercase      =    ConfigTester(self				,							config_class=lowerCamelCase__				,							has_text_modality=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						int:
													"""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     UpperCAmelCase_      ( self :     int				)    ->						Union[str, Any]:
													"""simple docstring"""


													return






						@unittest.skip(reason='''RegNet does not use inputs_embeds'''				)
						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Any:
													"""simple docstring"""


													pass






						@unittest.skip(reason='''RegNet does not support input and output embeddings'''				)
						def     UpperCAmelCase_      ( self :     int				)    ->						List[Any]:
													"""simple docstring"""


													pass






						def     UpperCAmelCase_      ( self :     int				)    ->						List[str]:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.model_tester.prepare_config_and_inputs_for_common()

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

																				__lowercase      =    ['''pixel_values''']
																				self.assertListEqual(arg_names[:1]				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     int				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase     ,     __lowercase      =    self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__lowercase      =    model_class(config=lowerCamelCase__				)
																				for name, module in model.named_modules():
																											if isinstance(lowerCamelCase__				,							(nn.BatchNormad, nn.GroupNorm)				):
																																		self.assertTrue(
																																		    torch.all(module.weight == 1				)				,							msg=f'Parameter {name} of model {model_class} seems not properly initialized'				,							)
																																		self.assertTrue(
																																		    torch.all(module.bias == 0				)				,							msg=f'Parameter {name} of model {model_class} seems not properly initialized'				,							)






						def     UpperCAmelCase_      ( self :     Any				)    ->						Union[str, Any]:
													"""simple docstring"""


													def check_hidden_states_output(lowerCamelCase__ :     str				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     List[Any]				):
																				__lowercase      =    model_class(lowerCamelCase__				)
																				model.to(lowerCamelCase__				)
																				model.eval()

																				with torch.no_grad():
																											__lowercase      =    model(**self._prepare_for_class(lowerCamelCase__				,							lowerCamelCase__				)				)

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

																				__lowercase      =    self.model_tester.num_stages
																				self.assertEqual(len(lowerCamelCase__				)				,							expected_num_stages + 1				)

																				# RegNet'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 // 2, self.model_tester.image_size // 2]				,							)

													__lowercase     ,     __lowercase      =    self.model_tester.prepare_config_and_inputs_for_common()
													__lowercase      =    ['''basic''', '''bottleneck''']
													for model_class in self.all_model_classes:
																				for layer_type in layers_type:
																											__lowercase      =    layer_type
																											__lowercase      =    True
																											check_hidden_states_output(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)

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

																											check_hidden_states_output(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__				)






						@slow
						def     UpperCAmelCase_      ( self :     Dict				)    ->						List[Any]:
													"""simple docstring"""


													for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__lowercase      =    RegNetModel.from_pretrained(lowerCamelCase__				)
																				self.assertIsNotNone(lowerCamelCase__				)







def 		_A(       )    -> Union[str, Any]:
							'''simple docstring'''


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







@require_torch
@require_vision
class a (     unittest.TestCase  ):
						"""simple docstring"""

						@cached_property
						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						List[Any]:
													"""simple docstring"""


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






						@slow
						def     UpperCAmelCase_      ( self :     Any				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(lowerCamelCase__				)

													__lowercase      =    self.default_image_processor
													__lowercase      =    prepare_img()
													__lowercase      =    image_processor(images=lowerCamelCase__				,							return_tensors='''pt'''				).to(lowerCamelCase__				)

													# forward pass
													with torch.no_grad():
																				__lowercase      =    model(**lowerCamelCase__				)

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

													__lowercase      =    torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]				).to(lowerCamelCase__				)

													self.assertTrue(torch.allclose(outputs.logits[0, :3]				,							lowerCamelCase__				,							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 		_A(       UpperCamelCase__							:			Tuple				)    -> Tuple:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			str				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    {}

							__lowercase      =    {
							    '''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''',
							}

							__lowercase      =    {
							    '''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
																					__lowercase      =    '''.'''.join(key[1:]				)

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

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

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

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

																					__lowercase      =    flax_dict[key]

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

							return converted_torch_dict







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[str]=False				,       UpperCamelCase__							:			List[str]=False				)    -> Any:
							'''simple docstring'''


							__lowercase      =    get_flax_param(UpperCamelCase__				)

							if not use_large:
														__lowercase      =    PixaStructVisionConfig()
														__lowercase      =    PixaStructTextConfig()
							else:
														__lowercase      =    PixaStructVisionConfig(
														    hidden_size=1536				,       d_ff=3968				,       num_attention_heads=24				,       num_hidden_layers=18				)
														__lowercase      =    PixaStructTextConfig(hidden_size=1536				,       d_ff=3968				,       num_heads=24				,       num_layers=18				)
							__lowercase      =    PixaStructConfig(
							    vision_config=encoder_config.to_dict()				,       text_config=decoder_config.to_dict()				,       is_vqa=UpperCamelCase__				)

							__lowercase      =    PixaStructForConditionalGeneration(UpperCamelCase__				)

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

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

							if use_large:
														__lowercase      =    4096

							__lowercase      =    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__":
			UpperCAmelCase__				     = 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.")
			UpperCAmelCase__				     = parser.parse_args()

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






