






import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 (
				    MODEL_MAPPING,
				    DataaVecVisionForImageClassification,
				    DataaVecVisionForSemanticSegmentation,
				    DataaVecVisionModel,
				)
				from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import BeitImageProcessor

class 			A_ :
	def __init__(					self							,      _A							,      _A=1_0_0							,      _A=1_3							,      _A=3_0							,      _A=2							,      _A=3							,      _A=True							,      _A=True							,      _A=3_2							,      _A=4							,      _A=4							,      _A=3_7							,      _A="gelu"							,      _A=0.1							,      _A=0.1							,      _A=1_0							,      _A=0.02							,      _A=3							,      _A=None							,      _A=[0, 1, 2, 3]							,      ):






				'''simple docstring'''







				UpperCAmelCase     	=       parent
				UpperCAmelCase     	=       1_0_0
				UpperCAmelCase     	=       batch_size
				UpperCAmelCase     	=       image_size
				UpperCAmelCase     	=       patch_size
				UpperCAmelCase     	=       num_channels
				UpperCAmelCase     	=       is_training
				UpperCAmelCase     	=       use_labels
				UpperCAmelCase     	=       hidden_size
				UpperCAmelCase     	=       num_hidden_layers
				UpperCAmelCase     	=       num_attention_heads
				UpperCAmelCase     	=       intermediate_size
				UpperCAmelCase     	=       hidden_act
				UpperCAmelCase     	=       hidden_dropout_prob
				UpperCAmelCase     	=       attention_probs_dropout_prob
				UpperCAmelCase     	=       type_sequence_label_size
				UpperCAmelCase     	=       initializer_range
				UpperCAmelCase     	=       scope
				UpperCAmelCase     	=       out_indices
				UpperCAmelCase     	=       num_labels

				# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				UpperCAmelCase     	=       (image_size // patch_size) ** 2
				UpperCAmelCase     	=       num_patches + 1
	def   _lowercase       (					self	):






				'''simple docstring'''







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

				UpperCAmelCase     	=       None
				UpperCAmelCase     	=       None
				if self.use_labels:
							UpperCAmelCase     	=       ids_tensor([self.batch_size]							,      self.type_sequence_label_size	)
							UpperCAmelCase     	=       ids_tensor([self.batch_size, self.image_size, self.image_size]							,      self.num_labels	)

				UpperCAmelCase     	=       self.get_config()

				return config, pixel_values, labels, pixel_labels
	def   _lowercase       (					self	):






				'''simple docstring'''







				return DataaVecVisionConfig(
				    vocab_size=self.vocab_size							,      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=_A							,      initializer_range=self.initializer_range							,      out_indices=self.out_indices							,      )
	def   _lowercase       (					self							,      _A							,      _A							,      _A							,      _A	):






				'''simple docstring'''







				UpperCAmelCase     	=       DataaVecVisionModel(config=_A	)
				model.to(_A	)
				model.eval()
				UpperCAmelCase     	=       model(_A	)
				# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
				UpperCAmelCase     	=       (self.image_size // self.patch_size) ** 2
				self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, num_patches + 1, self.hidden_size)	)
	def   _lowercase       (					self							,      _A							,      _A							,      _A							,      _A	):






				'''simple docstring'''







				UpperCAmelCase     	=       self.type_sequence_label_size
				UpperCAmelCase     	=       DataaVecVisionForImageClassification(_A	)
				model.to(_A	)
				model.eval()
				UpperCAmelCase     	=       model(_A							,      labels=_A	)
				self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.type_sequence_label_size)	)
	def   _lowercase       (					self							,      _A							,      _A							,      _A							,      _A	):






				'''simple docstring'''







				UpperCAmelCase     	=       self.num_labels
				UpperCAmelCase     	=       DataaVecVisionForSemanticSegmentation(_A	)
				model.to(_A	)
				model.eval()
				UpperCAmelCase     	=       model(_A	)
				self.parent.assertEqual(
				    result.logits.shape							,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
				UpperCAmelCase     	=       model(_A							,      labels=_A	)
				self.parent.assertEqual(
				    result.logits.shape							,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)







	def   _lowercase       (					self	):






				'''simple docstring'''







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

@require_torch
class 			A_ (lowercase_  ,							lowercase_  ,							unittest.TestCase    ):
	UpperCAmelCase__      =    (
	    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
	    if is_torch_available()
	    else ()
	)
	UpperCAmelCase__      =    (
	    {
	        """feature-extraction""": DataaVecVisionModel,
	        """image-classification""": DataaVecVisionForImageClassification,
	        """image-segmentation""": DataaVecVisionForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	UpperCAmelCase__      =    False
	UpperCAmelCase__      =    False
	UpperCAmelCase__      =    False
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       DataaVecVisionModelTester(self	)
				UpperCAmelCase     	=       ConfigTester(
				    self							,      config_class=_A							,      has_text_modality=_A							,      hidden_size=3_7	)
	def   _lowercase       (					self	):






				'''simple docstring'''







				self.config_tester.run_common_tests()
	def   _lowercase       (					self	):






				'''simple docstring'''







				pass
	@require_torch_multi_gpu
	@unittest.skip(
	    reason='''Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''	)
	def   _lowercase       (					self	):






				'''simple docstring'''







				pass
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase	,    UpperCAmelCase     	=       self.model_tester.prepare_config_and_inputs_for_common()

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






				'''simple docstring'''







				UpperCAmelCase	,    UpperCAmelCase     	=       self.model_tester.prepare_config_and_inputs_for_common()

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

							UpperCAmelCase     	=       ['''pixel_values''']
							self.assertListEqual(arg_names[:1]							,      _A	)
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_A	)
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_segmentation(*_A	)
	def   _lowercase       (					self	):






				'''simple docstring'''







				if not self.model_tester.is_training:
							return

				UpperCAmelCase	,    UpperCAmelCase     	=       self.model_tester.prepare_config_and_inputs_for_common()
				UpperCAmelCase     	=       True

				for model_class in self.all_model_classes:
							if model_class in [*get_values(_A	)]:
										continue

							UpperCAmelCase     	=       model_class(_A	)
							model.to(_A	)
							model.train()
							UpperCAmelCase     	=       self._prepare_for_class(_A							,      _A							,      return_labels=_A	)
							UpperCAmelCase     	=       model(**_A	).loss
							loss.backward()
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase	,    UpperCAmelCase     	=       self.model_tester.prepare_config_and_inputs_for_common()
				if not self.model_tester.is_training:
							return

				UpperCAmelCase     	=       False
				UpperCAmelCase     	=       True

				for model_class in self.all_model_classes:
							if model_class in [*get_values(_A	)] or not model_class.supports_gradient_checkpointing:
										continue
							# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
							# this can then be incorporated into _prepare_for_class in test_modeling_common.py
							elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
										UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     	=       inputs_dict['''pixel_values'''].shape
										UpperCAmelCase     	=       torch.zeros(
										    [self.model_tester.batch_size, height, width]							,      device=_A	).long()
							UpperCAmelCase     	=       model_class(_A	)
							model.gradient_checkpointing_enable()
							model.to(_A	)
							model.train()
							UpperCAmelCase     	=       self._prepare_for_class(_A							,      _A							,      return_labels=_A	)
							UpperCAmelCase     	=       model(**_A	).loss
							loss.backward()
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase	,    UpperCAmelCase     	=       self.model_tester.prepare_config_and_inputs_for_common()

				UpperCAmelCase     	=       _config_zero_init(_A	)
				for model_class in self.all_model_classes:
							UpperCAmelCase     	=       model_class(config=_A	)
							for name, param in model.named_parameters():
										# we skip lambda parameters as these require special initial values
										# determined by config.layer_scale_init_value
										if "lambda" in name:
													continue
										if param.requires_grad:
													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"""							,      )
	def   _lowercase       (					self							,      _A							,      _A							,      _A							,      _A=2E-4							,      _A="outputs"							,      _A=None	):






				'''simple docstring'''







				super().check_pt_tf_outputs(_A							,      _A							,      _A							,      _A							,      _A							,      _A	)
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_A	)







	@slow
	def   _lowercase       (					self	):






				'''simple docstring'''







				for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase     	=       DataaVecVisionModel.from_pretrained(_A	)
							self.assertIsNotNone(_A	)





def 		__SCREAMING_SNAKE_CASE						(       )						->       Dict:







			'''simple docstring'''

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






@require_torch
@require_vision
class 			A_ (unittest.TestCase    ):
	@cached_property
	def   _lowercase       (					self	):






				'''simple docstring'''







				return (
				    BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k'''	) if is_vision_available() else None
				)







	@slow
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k'''	).to(
				    _A	)

				UpperCAmelCase     	=       self.default_image_processor
				UpperCAmelCase     	=       prepare_img()
				UpperCAmelCase     	=       image_processor(images=_A							,      return_tensors='''pt'''	).to(_A	)

				# forward pass
				with torch.no_grad():
							UpperCAmelCase     	=       model(**_A	)
				UpperCAmelCase     	=       outputs.logits

				# verify the logits
				UpperCAmelCase     	=       torch.Size((1, 1_0_0_0)	)
				self.assertEqual(logits.shape							,      _A	)

				UpperCAmelCase     	=       torch.tensor([0.32_77, -0.13_95, 0.09_11]	).to(_A	)

				self.assertTrue(torch.allclose(logits[0, :3]							,      _A							,      atol=1E-4	)	)

				UpperCAmelCase     	=       [model.config.labelaid[i] for i in ['''remote control, remote''', '''tabby, tabby cat''']]
				self.assertEqual(logits[0].topk(2	).indices.cpu().tolist()							,      _A	)










import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)						->       Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase     	=       TaConfig.from_pretrained(UpperCamelCase__				)
			UpperCAmelCase     	=       FlaxTaForConditionalGeneration(config=UpperCamelCase__				)
			UpperCAmelCase     	=       checkpoints.load_tax_checkpoint(UpperCamelCase__				)

			UpperCAmelCase     	=       '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

			# Encoder
			for layer_index in range(config.num_layers				):
						UpperCAmelCase     	=       F"""layers_{str(UpperCamelCase__				)}"""

						# Self-Attention
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

						if split_mlp_wi:
									UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						UpperCAmelCase     	=       tax_attention_key
						UpperCAmelCase     	=       tax_attention_out
						UpperCAmelCase     	=       tax_attention_query
						UpperCAmelCase     	=       tax_attention_value

						UpperCAmelCase     	=       tax_attention_layer_norm

						if split_mlp_wi:
									UpperCAmelCase     	=       tax_mlp_wi_a
									UpperCAmelCase     	=       tax_mlp_wi_a
						else:
									UpperCAmelCase     	=       tax_mlp_wi

						UpperCAmelCase     	=       tax_mlp_wo
						UpperCAmelCase     	=       tax_mlp_layer_norm

			# Only for layer 0:
			UpperCAmelCase     	=       tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
			UpperCAmelCase     	=       tax_encoder_rel_embedding

			# Assigning
			UpperCAmelCase     	=       tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
			UpperCAmelCase     	=       tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_decoder_layers				):
						UpperCAmelCase     	=       F"""layers_{str(UpperCamelCase__				)}"""

						# Self-Attention
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
						    '''scale'''
						]

						# Encoder-Decoder-Attention
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
						    '''kernel'''
						]
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
						    '''kernel'''
						]
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
						    '''kernel'''
						]
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
						    '''kernel'''
						]

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

						# MLP
						if split_mlp_wi:
									UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						UpperCAmelCase     	=       tax_attention_key
						UpperCAmelCase     	=       tax_attention_out
						UpperCAmelCase     	=       tax_attention_query
						UpperCAmelCase     	=       tax_attention_value

						UpperCAmelCase     	=       tax_pre_attention_layer_norm

						UpperCAmelCase     	=       tax_enc_dec_attention_key
						UpperCAmelCase     	=       tax_enc_dec_attention_out
						UpperCAmelCase     	=       tax_enc_dec_attention_query
						UpperCAmelCase     	=       tax_enc_dec_attention_value

						UpperCAmelCase     	=       tax_cross_layer_norm

						if split_mlp_wi:
									UpperCAmelCase     	=       tax_mlp_wi_a
									UpperCAmelCase     	=       tax_mlp_wi_a
						else:
									UpperCAmelCase     	=       tax_mlp_wi

						UpperCAmelCase     	=       tax_mlp_wo

						UpperCAmelCase     	=       txa_mlp_layer_norm

			# Decoder Normalization
			UpperCAmelCase     	=       tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
			UpperCAmelCase     	=       txa_decoder_norm

			# Only for layer 0:
			UpperCAmelCase     	=       tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
			UpperCAmelCase     	=       tax_decoder_rel_embedding

			# Token Embeddings
			UpperCAmelCase     	=       tax_model['''target''']['''token_embedder''']['''embedding''']
			UpperCAmelCase     	=       txa_token_embeddings

			# LM Head (only in v1.1 checkpoints)
			if "logits_dense" in tax_model["target"]["decoder"]:
						UpperCAmelCase     	=       tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

			flax_model.save_pretrained(UpperCamelCase__				)
			print('''T5X Model was sucessfully converted!'''				)


if __name__ == "__main__":
				__A       :					Union[str, Any]     					=     argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
				)
				parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
				parser.add_argument(
				    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
				)
				__A       :					Any     					=     parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)



