


from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor






class      UpperCamelCase					:


			'''simple docstring'''







			def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_=3   ,							UpperCamelCase_=32   ,							UpperCamelCase_=3   ,							UpperCamelCase_=10   ,							UpperCamelCase_=[10, 20, 30, 40]   ,							UpperCamelCase_=[1, 1, 2, 1]   ,							UpperCamelCase_=True   ,							UpperCamelCase_=True   ,							UpperCamelCase_="relu"   ,							UpperCamelCase_=3   ,							UpperCamelCase_=None   ,							):
								lowercase_       :List[str]     =       parent
								lowercase_       :Dict     =       batch_size
								lowercase_       :Optional[Any]     =       image_size
								lowercase_       :str     =       num_channels
								lowercase_       :Union[str, Any]     =       embeddings_size
								lowercase_       :Optional[int]     =       hidden_sizes
								lowercase_       :List[str]     =       depths
								lowercase_       :Tuple     =       is_training
								lowercase_       :str     =       use_labels
								lowercase_       :List[str]     =       hidden_act
								lowercase_       :int     =       num_labels
								lowercase_       :str     =       scope
								lowercase_       :List[str]     =       len(UpperCamelCase_		)
			def 		UpperCamelCase  (     self		):
								lowercase_       :Union[str, Any]     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

								lowercase_       :int     =       None
								if self.use_labels:
													lowercase_       :Dict     =       ids_tensor([self.batch_size]   ,							self.num_labels		)

								lowercase_       :Tuple     =       self.get_config()
								return config, pixel_values, labels
			def 		UpperCamelCase  (     self		):
								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   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
								lowercase_       :Tuple     =       TFRegNetModel(config=UpperCamelCase_		)
								lowercase_       :Dict     =       model(UpperCamelCase_   ,							training=UpperCamelCase_		)
								# 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   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
								lowercase_       :List[Any]     =       self.num_labels
								lowercase_       :List[str]     =       TFRegNetForImageClassification(UpperCamelCase_		)
								lowercase_       :Any     =       model(UpperCamelCase_   ,							labels=UpperCamelCase_   ,							training=UpperCamelCase_		)
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)




			def 		UpperCamelCase  (     self		):
								lowercase_       :str     =       self.prepare_config_and_inputs()
								lowercase_       :List[Any]     =       config_and_inputs
								lowercase_       :Union[str, Any]     =       {'''pixel_values''': pixel_values}
								return config, inputs_dict






@require_tf
class      UpperCamelCase					(		lowercase__						,      lowercase__						,      unittest.TestCase     ):


			'''simple docstring'''







			lowercase					:	List[Any]									=(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
			lowercase					:	Dict									=(
			    {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
			    if is_tf_available()
			    else {}
			)

			lowercase					:	Optional[int]									=False
			lowercase					:	int									=False
			lowercase					:	str									=False
			lowercase					:	Dict									=False
			lowercase					:	str									=False
			def 		UpperCamelCase  (     self		):
								lowercase_       :List[str]     =       TFRegNetModelTester(self		)
								lowercase_       :Tuple     =       ConfigTester(self   ,							config_class=UpperCamelCase_   ,							has_text_modality=UpperCamelCase_		)
			def 		UpperCamelCase  (     self		):
								return
			@unittest.skip(reason='''RegNet does not use inputs_embeds'''		)
			def 		UpperCamelCase  (     self		):
								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.'''   ,							)
			@slow
			def 		UpperCamelCase  (     self		):
								super().test_keras_fit()
			@unittest.skip(reason='''RegNet does not support input and output embeddings'''		)
			def 		UpperCamelCase  (     self		):
								pass
			def 		UpperCamelCase  (     self		):
								lowercase_       :List[Any]     =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													lowercase_       :Union[str, Any]     =       model_class(UpperCamelCase_		)
													lowercase_       :Optional[int]     =       inspect.signature(model.call		)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowercase_       :Optional[int]     =       [*signature.parameters.keys()]

													lowercase_       :Dict     =       ['''pixel_values''']
													self.assertListEqual(arg_names[:1]   ,							UpperCamelCase_		)
			def 		UpperCamelCase  (     self		):
								lowercase_       :Optional[Any]     =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase_		)
			def 		UpperCamelCase  (     self		):
								def check_hidden_states_output(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
													lowercase_       :Union[str, Any]     =       model_class(UpperCamelCase_		)
													lowercase_       :List[str]     =       model(**self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_		)   ,							training=UpperCamelCase_		)

													lowercase_       :Tuple     =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													lowercase_       :List[str]     =       self.model_tester.num_stages
													self.assertEqual(len(UpperCamelCase_		)   ,							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_       :List[Any]     =       self.model_tester.prepare_config_and_inputs_for_common()
								lowercase_       :Union[str, Any]     =       ['''basic''', '''bottleneck''']
								for model_class in self.all_model_classes:
													for layer_type in layers_type:
																		lowercase_       :str     =       layer_type
																		lowercase_       :Dict     =       True
																		check_hidden_states_output(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		lowercase_       :Optional[int]     =       True

																		check_hidden_states_output(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)
			def 		UpperCamelCase  (     self		):
								lowercase_       :int     =       self.model_tester.prepare_config_and_inputs_for_common()

								def check_equivalence(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_={}		):
													lowercase_       :str     =       model(UpperCamelCase_   ,							return_dict=UpperCamelCase_   ,							**UpperCamelCase_		)
													lowercase_       :Tuple     =       model(UpperCamelCase_   ,							return_dict=UpperCamelCase_   ,							**UpperCamelCase_		).to_tuple()

													def recursive_check(UpperCamelCase_   ,							UpperCamelCase_		):
																		if isinstance(UpperCamelCase_   ,							(List, Tuple)		):
																							for tuple_iterable_value, dict_iterable_value in zip(UpperCamelCase_   ,							UpperCamelCase_		):
																												recursive_check(UpperCamelCase_   ,							UpperCamelCase_		)
																		elif tuple_object is None:
																							return
																		else:
																							self.assertTrue(
																							    all(tf.equal(UpperCamelCase_   ,							UpperCamelCase_		)		)   ,							msg=(
																							        '''Tuple and dict output are not equal. Difference:'''
																							        f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object		)		)}"
																							    )   ,							)

																		recursive_check(UpperCamelCase_   ,							UpperCamelCase_		)

								for model_class in self.all_model_classes:
													lowercase_       :str     =       model_class(UpperCamelCase_		)

													lowercase_       :int     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_		)
													lowercase_       :List[str]     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_		)
													check_equivalence(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)

													lowercase_       :Dict     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_   ,							return_labels=UpperCamelCase_		)
													lowercase_       :Optional[Any]     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_   ,							return_labels=UpperCamelCase_		)
													check_equivalence(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)

													lowercase_       :Optional[Any]     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_		)
													lowercase_       :List[str]     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_		)
													check_equivalence(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							{'''output_hidden_states''': True}		)

													lowercase_       :int     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_   ,							return_labels=UpperCamelCase_		)
													lowercase_       :Any     =       self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_   ,							return_labels=UpperCamelCase_		)
													check_equivalence(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							{'''output_hidden_states''': True}		)
			def 		UpperCamelCase  (     self		):
								lowercase_       :str     =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_		)




			@slow
			def 		UpperCamelCase  (     self		):
								for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase_       :Optional[Any]     =       TFRegNetModel.from_pretrained(UpperCamelCase_		)
													self.assertIsNotNone(UpperCamelCase_		)




def     UpperCamelCase    (      )					->							Optional[int]:
					'''simple docstring'''





					lowercase_       :Optional[Any]     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
					return image






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


			'''simple docstring'''







			@cached_property
			def 		UpperCamelCase  (     self		):
								return (
								    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
								    if is_vision_available()
								    else None
								)




			@slow
			def 		UpperCamelCase  (     self		):
								lowercase_       :List[Any]     =       TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)

								lowercase_       :Optional[Any]     =       self.default_image_processor
								lowercase_       :Optional[Any]     =       prepare_img()
								lowercase_       :str     =       image_processor(images=UpperCamelCase_   ,							return_tensors='''tf'''		)

								# forward pass
								lowercase_       :Any     =       model(**UpperCamelCase_   ,							training=UpperCamelCase_		)

								# verify the logits
								lowercase_       :List[Any]     =       tf.TensorShape((1, 1000)		)
								self.assertEqual(outputs.logits.shape   ,							UpperCamelCase_		)

								lowercase_       :int     =       tf.constant([-0.4180, -1.5051, -3.4836]		)

								tf.debugging.assert_near(outputs.logits[0, :3]   ,							UpperCamelCase_   ,							atol=1E-4		)





import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def     UpperCamelCase    (      _a       ,		_a       ,		_a=None		)					->							Dict:
     '''simple docstring'''





     assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
     lowercase_       :str     =       nn.Parameter(_a		)
     if bias is not None:
          assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
          lowercase_       :List[str]     =       nn.Parameter(_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							List[str]:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       np.asarray(weights[0]		)
     lowercase_       :str     =       np.asarray(weights[1]		)
     lowercase_       :Union[str, Any]     =       np.asarray(weights[2]		)

     set_param(
         torch_layer.self_attention.query_key       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.value       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.output.dense       ,		torch.tensor(_a		).view(-1       ,		_a		).contiguous().transpose(0       ,		1		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[int]:
     '''simple docstring'''





     lowercase_       :Union[str, Any]     =       np.asarray(weights[0]		)
     lowercase_       :Tuple     =       np.asarray(weights[1]		)
     lowercase_       :List[Any]     =       np.asarray(weights[2]		)
     lowercase_       :Optional[int]     =       np.asarray(weights[3]		)

     set_param(
         torch_layer.self_attention.query       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.key       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.value       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.output.dense       ,		torch.tensor(_a		).view(-1       ,		_a		).contiguous().transpose(0       ,		1		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :List[str]     =       weights[0][0][0]
     lowercase_       :Optional[Any]     =       np.asarray(layer_norm_a[0]		)
     lowercase_       :Any     =       np.asarray(layer_norm_a[1]		)
     set_param(
         torch_block.attention.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # lsh weights + output
     lowercase_       :int     =       weights[0][1]
     if len(_a		) < 4:
          set_layer_weights_in_torch_lsh(_a       ,		torch_block.attention       ,		_a		)
     else:
          set_layer_weights_in_torch_local(_a       ,		torch_block.attention       ,		_a		)

     # intermediate weighs
     lowercase_       :Optional[int]     =       weights[2][0][1][2]

     # Chunked Feed Forward
     if len(_a		) == 4:
          lowercase_       :Tuple     =       intermediate_weights[2]

     # layernorm 2
     lowercase_       :int     =       np.asarray(intermediate_weights[0][0]		)
     lowercase_       :Any     =       np.asarray(intermediate_weights[0][1]		)
     set_param(
         torch_block.feed_forward.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # intermediate dense
     lowercase_       :Any     =       np.asarray(intermediate_weights[1][0]		)
     lowercase_       :int     =       np.asarray(intermediate_weights[1][1]		)
     set_param(
         torch_block.feed_forward.dense.dense       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)

     # intermediate out
     lowercase_       :Optional[int]     =       np.asarray(intermediate_weights[4][0]		)
     lowercase_       :Any     =       np.asarray(intermediate_weights[4][1]		)
     set_param(
         torch_block.feed_forward.output.dense       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Any     =       torch_model.reformer

     # word embeds
     lowercase_       :int     =       np.asarray(weights[1]		)
     set_param(
         torch_model_reformer.embeddings.word_embeddings       ,		torch.tensor(_a		)       ,		)

     if isinstance(weights[3]       ,		_a		):
          lowercase_       :Union[str, Any]     =       torch_model_reformer.embeddings.position_embeddings
          for emb_idx in range(len(position_embeddings.weights		)		):
               lowercase_       :Tuple     =       np.asarray(weights[3][emb_idx][0]		)
               assert (
                   position_embeddings.weights[emb_idx].shape == emb_weights.shape
               ), f"{position_embeddings[emb_idx]} emb does not match"
               lowercase_       :Optional[int]     =       nn.Parameter(torch.tensor(_a		)		)

     lowercase_       :Any     =       weights[5]
     assert len(torch_model_reformer.encoder.layers		) * 4 == len(
         _a		), "HF and trax model do not have the same number of layers"
     for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers		):
          lowercase_       :int     =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
          set_block_weights_in_torch(_a       ,		_a       ,		_a		)

     # output layer norm
     lowercase_       :Optional[Any]     =       np.asarray(weights[7][0]		)
     lowercase_       :int     =       np.asarray(weights[7][1]		)
     set_param(
         torch_model_reformer.encoder.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # output embeddings
     lowercase_       :Optional[int]     =       np.asarray(weights[9][0]		)
     lowercase_       :Union[str, Any]     =       np.asarray(weights[9][1]		)
     set_param(
         torch_model.lm_head.decoder       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							str:
     '''simple docstring'''





     lowercase_       :List[str]     =       ReformerConfig.from_json_file(_a		)
     print(f"Building PyTorch model from configuration: {config}"		)
     lowercase_       :List[str]     =       ReformerModelWithLMHead(_a		)

     with open(_a       ,		'''rb'''		) as f:
          lowercase_       :Dict     =       pickle.load(_a		)['''weights''']

     set_model_weights_in_torch(_a       ,		_a       ,		config.hidden_size		)

     # Save pytorch-model
     print(f"Save PyTorch model to {pytorch_dump_path}"		)
     torch.save(model.state_dict()       ,		_a		)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--config_file",
       default=None,
       type=str,
       required=True,
       help=(
           "The config json file corresponding to the pre-trained Reformer model. \n"
           "This specifies the model architecture."
       ),
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   SCREAMING_SNAKE_CASE       :		Dict         =				parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)

