
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  Optional[int]:
		'''simple docstring'''

		UpperCamelCase		=     AutoConfig.from_pretrained(lowercase )
		UpperCamelCase		=     FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase )
		UpperCamelCase		=     checkpoints.load_tax_checkpoint(lowercase )

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

		if config.model_type == "t5":
				UpperCamelCase		=     'SelfAttention'
		if config.model_type == "longt5" and config.encoder_attention_type == "local":
				UpperCamelCase		=     'LocalSelfAttention'
		elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				UpperCamelCase		=     'TransientGlobalSelfAttention'
		else:
				raise ValueError(
				    'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
				    ' attribute with a value from [\'local\', \'transient-global].' )

		# Encoder
		for layer_index in range(config.num_layers ):
				UpperCamelCase		=     f'''layers_{str(lowercase )}'''

				# 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']

				# Global input layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						UpperCamelCase		=     tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

				# 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		=     flax_model.params['encoder']['block'][str(lowercase )]['layer']
				UpperCamelCase		=     tax_attention_key
				UpperCamelCase		=     tax_attention_out
				UpperCamelCase		=     tax_attention_query
				UpperCamelCase		=     tax_attention_value

				UpperCamelCase		=     tax_attention_layer_norm

				# Global input layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						UpperCamelCase		=     tax_global_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

				UpperCamelCase		=     flax_model_encoder_layer_block

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

		# Side/global relative position_bias + layer norm
		if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				UpperCamelCase		=     tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
				UpperCamelCase		=     tax_encoder_global_rel_embedding

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

		# Decoder
		for layer_index in range(config.num_layers ):
				UpperCamelCase		=     f'''layers_{str(lowercase )}'''

				# 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']
				UpperCamelCase		=     tax_enc_dec_attention_module['key']['kernel']
				UpperCamelCase		=     tax_enc_dec_attention_module['out']['kernel']
				UpperCamelCase		=     tax_enc_dec_attention_module['query']['kernel']
				UpperCamelCase		=     tax_enc_dec_attention_module['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		=     flax_model.params['decoder']['block'][str(lowercase )]['layer']
				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

				UpperCamelCase		=     flax_model_decoder_layer_block

		# 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 and LongT5 checkpoints)
		if "logits_dense" in tax_model["target"]["decoder"]:
				UpperCamelCase		=     tax_model['target']['decoder']['logits_dense']['kernel']

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


if __name__ == "__main__":
					_UpperCAmelCase					: Dict      =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
					)
					parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
					parser.add_argument(
					    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
					)
					_UpperCAmelCase					: Tuple      =       parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


import inspect
import unittest
from math import floor

from transformers import CvtConfig
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 transformers import CvtForImageClassification, CvtModel
					from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     self.config_class(**self.inputs_dict       )
				self.parent.assertTrue(hasattr(A_				,      'embed_dim'       )       )
				self.parent.assertTrue(hasattr(A_				,      'num_heads'       )       )






class 				lowercase					:



		def __init__( self				,      A_				,      A_=13				,      A_=64				,      A_=3				,      A_=[16, 48, 96]				,      A_=[1, 3, 6]				,      A_=[1, 2, 10]				,      A_=[7, 3, 3]				,      A_=[4, 2, 2]				,      A_=[2, 1, 1]				,      A_=[2, 2, 2]				,      A_=[False, False, True]				,      A_=[0.0, 0.0, 0.0]				,      A_=0.02				,      A_=1e-12				,      A_=True				,      A_=True				,      A_=2				,      )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     parent
				UpperCamelCase		=     batch_size
				UpperCamelCase		=     image_size
				UpperCamelCase		=     patch_sizes
				UpperCamelCase		=     patch_stride
				UpperCamelCase		=     patch_padding
				UpperCamelCase		=     is_training
				UpperCamelCase		=     use_labels
				UpperCamelCase		=     num_labels
				UpperCamelCase		=     num_channels
				UpperCamelCase		=     embed_dim
				UpperCamelCase		=     num_heads
				UpperCamelCase		=     stride_kv
				UpperCamelCase		=     depth
				UpperCamelCase		=     cls_token
				UpperCamelCase		=     attention_drop_rate
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     layer_norm_eps



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

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



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""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 __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     CvtModel(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_       )
				UpperCamelCase		=     (self.image_size, self.image_size)
				UpperCamelCase	,  UpperCamelCase		=     image_size[0], image_size[1]
				for i in range(len(self.depth       )       ):
						UpperCamelCase		=     floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
						UpperCamelCase		=     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 __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.num_labels
				UpperCamelCase		=     CvtForImageClassification(A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_				,      labels=A_       )
				self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_labels)       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.prepare_config_and_inputs()
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     config_and_inputs
				UpperCamelCase		=     {'pixel_values': pixel_values}
				return config, inputs_dict






@require_torch
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       List[str]	     =				(CvtModel, CvtForImageClassification) if is_torch_available() else ()
		__lowercase       :       Tuple	     =				(
		    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
		    if is_torch_available()
		    else {}
		)

		__lowercase       :       Tuple	     =				False
		__lowercase       :       Union[str, Any]	     =				False
		__lowercase       :       Optional[Any]	     =				False
		__lowercase       :       List[str]	     =				False
		__lowercase       :       Dict	     =				False



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     CvtModelTester(self       )
				UpperCamelCase		=     ConfigTester(self				,      config_class=A_				,      has_text_modality=A_				,      hidden_size=37       )



		def __UpperCamelCase ( self       )  ->					str:
				"""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       )  ->					Any:
				"""simple docstring"""
				return



		@unittest.skip(reason='Cvt does not output attentions'       )
		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				pass



		@unittest.skip(reason='Cvt does not use inputs_embeds'       )
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				pass



		@unittest.skip(reason='Cvt does not support input and output embeddings'       )
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""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 __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				def check_hidden_states_output(A_				,      A_				,      A_       ):
						UpperCamelCase		=     model_class(A_       )
						model.to(A_       )
						model.eval()

						with torch.no_grad():
								UpperCamelCase		=     model(**self._prepare_for_class(A_				,      A_       )       )

						UpperCamelCase		=     outputs.hidden_states

						UpperCamelCase		=     len(self.model_tester.depth       )
						self.assertEqual(len(A_       )				,      A_       )

						# 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,
						    ]				,      )

				UpperCamelCase	,  UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase		=     True
						check_hidden_states_output(A_				,      A_				,      A_       )

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

						check_hidden_states_output(A_				,      A_				,      A_       )



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*A_       )



		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				pass



		@slow
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase		=     CvtModel.from_pretrained(A_       )
						self.assertIsNotNone(A_       )






def       A       (					)   ->  Tuple:
		'''simple docstring'''

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


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



		@cached_property
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )



		@slow
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).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_       )

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

				UpperCamelCase		=     torch.tensor([0.9285, 0.9015, -0.3150]       ).to(A_       )

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