


"""simple docstring"""


import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax
		import jax.numpy as jnp

		from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor
class snake_case      (  unittest.TestCase):







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





						'''simple docstring'''







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







		def  a_ (					self      :       List[Any]		)       ->						str:





						'''simple docstring'''







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

						_A					  =			self.get_config()

						return config, pixel_values







		def  a_ (					self      :       Optional[int]		)       ->						List[str]:





						'''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    ,   image_size=self.image_size    ,   )







		def  a_ (					self      :       str    ,   a__      :       Any    ,   a__      :       Optional[int]		)       ->						Dict:





						'''simple docstring'''







						_A					  =			FlaxRegNetModel(config=SCREAMING_SNAKE_CASE_		)
						_A					  =			model(SCREAMING_SNAKE_CASE_		)

						# Output shape (b, c, h, w)
						self.parent.assertEqual(
						    result.last_hidden_state.shape    ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,   )







		def  a_ (					self      :       Union[str, Any]    ,   a__      :       str    ,   a__      :       str		)       ->						Dict:





						'''simple docstring'''







						_A					  =			self.num_labels
						_A					  =			FlaxRegNetForImageClassification(config=SCREAMING_SNAKE_CASE_		)
						_A					  =			model(SCREAMING_SNAKE_CASE_		)
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_labels)		)







		def  a_ (					self      :       Any		)       ->						Any:





						'''simple docstring'''







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





@require_flax
class snake_case      (  __lowerCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

		__UpperCamelCase           =			False
		__UpperCamelCase           =			False
		__UpperCamelCase           =			False







		def  a_ (					self      :       Optional[Any]		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			FlaxRegNetModelTester(self		)
						_A					  =			ConfigTester(self    ,   config_class=SCREAMING_SNAKE_CASE_    ,   has_text_modality=SCREAMING_SNAKE_CASE_		)







		def  a_ (					self      :       Any		)       ->						Union[str, Any]:





						'''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  a_ (					self      :       int		)       ->						Optional[Any]:





						'''simple docstring'''







						return







		def  a_ (					self      :       Optional[int]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_		)







		def  a_ (					self      :       List[str]		)       ->						Optional[int]:





						'''simple docstring'''







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







		@unittest.skip(reason="RegNet does not use inputs_embeds"		)
		def  a_ (					self      :       Union[str, Any]		)       ->						Optional[int]:





						'''simple docstring'''







						pass







		@unittest.skip(reason="RegNet does not support input and output embeddings"		)
		def  a_ (					self      :       Union[str, Any]		)       ->						List[str]:





						'''simple docstring'''







						pass







		def  a_ (					self      :       Optional[Any]		)       ->						List[str]:





						'''simple docstring'''







						_A  ,		_A					  =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										_A					  =			model_class(SCREAMING_SNAKE_CASE_		)
										_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]    ,   SCREAMING_SNAKE_CASE_		)







		def  a_ (					self      :       Union[str, Any]		)       ->						int:





						'''simple docstring'''







						def check_hidden_states_output(a__      :       Any    ,   a__      :       Any    ,   a__      :       int		):
										_A					  =			model_class(SCREAMING_SNAKE_CASE_		)

										_A					  =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,   SCREAMING_SNAKE_CASE_		)		)

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

										_A					  =			self.model_tester.num_stages
										self.assertEqual(len(SCREAMING_SNAKE_CASE_		)    ,   expected_num_stages + 1		)

						_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(SCREAMING_SNAKE_CASE_    ,   SCREAMING_SNAKE_CASE_    ,   SCREAMING_SNAKE_CASE_		)

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

										check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,   SCREAMING_SNAKE_CASE_    ,   SCREAMING_SNAKE_CASE_		)







		def  a_ (					self      :       Optional[Any]		)       ->						List[str]:





						'''simple docstring'''







						_A  ,		_A					  =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__		):
														_A					  =			self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,   SCREAMING_SNAKE_CASE_		)
														_A					  =			model_class(SCREAMING_SNAKE_CASE_		)

														@jax.jit
														def model_jitted(a__      :       List[Any]    ,   **a__      :       Optional[Any]		):
																		return model(pixel_values=SCREAMING_SNAKE_CASE_    ,   **SCREAMING_SNAKE_CASE_		)

														with self.subTest("JIT Enabled"		):
																		_A					  =			model_jitted(**SCREAMING_SNAKE_CASE_		).to_tuple()

														with self.subTest("JIT Disabled"		):
																		with jax.disable_jit():
																						_A					  =			model_jitted(**SCREAMING_SNAKE_CASE_		).to_tuple()

														self.assertEqual(len(SCREAMING_SNAKE_CASE_		)    ,   len(SCREAMING_SNAKE_CASE_		)		)
														for jitted_output, output in zip(SCREAMING_SNAKE_CASE_    ,   SCREAMING_SNAKE_CASE_		):
																		self.assertEqual(jitted_output.shape    ,   output.shape		)






def      a__ (				)	->	Tuple:
				_A					  =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
				return image




@require_flax
class snake_case      (  unittest.TestCase):







		@cached_property
		def  a_ (					self      :       Tuple		)       ->						Union[str, Any]:





						'''simple docstring'''







						return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"		) if is_vision_available() else None







		@slow
		def  a_ (					self      :       List[str]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"		)

						_A					  =			self.default_image_processor
						_A					  =			prepare_img()
						_A					  =			image_processor(images=SCREAMING_SNAKE_CASE_    ,   return_tensors="np"		)

						_A					  =			model(**SCREAMING_SNAKE_CASE_		)

						# verify the logits
						_A					  =			(1, 10_00)
						self.assertEqual(outputs.logits.shape    ,   SCREAMING_SNAKE_CASE_		)

						_A					  =			jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]		)

						self.assertTrue(jnp.allclose(outputs.logits[0, :3]    ,   SCREAMING_SNAKE_CASE_    ,   atol=1E-4		)		)



"""simple docstring"""


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def      a__ (				__lowercase   )	->	List[Any]:
				_A					  =			os.path.join(args.tf_model_dir     ,			"parameters.json"   )
				_A					  =			json.loads(open(__lowercase   ).read()   )
				if not params:
								raise ValueError(
								    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""   )
				if not args.output.endswith(".pt"   ):
								_A					  =			args.output + ".pt"
				_A					  =			OrderedDict()
				with tf.device("/CPU:0"   ):
								_A					  =			tf.train.load_checkpoint(args.tf_model_dir   )
								_A					  =			reader.get_variable_to_shape_map()
								for key_name in shapes.keys():
												_A					  =			reader.get_tensor(__lowercase   ).astype(np.floataa   )
												if key_name.endswith("/adam_m"   ) or key_name.endswith("/adam_v"   ):
																continue
												if key_name.startswith("pasts/"   ):
																if key_name.startswith("pasts/mlp"   ):
																				_A					  =			int(key_name[9]   )
																elif key_name.startswith("pasts/out"   ):
																				_A					  =			8
																_A					  =			"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/moe"   ):
																_A					  =			int(key_name[9:].split("/"   )[0]   )
																if key_name.endswith("/switch_gating/kernel"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
																				_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/softmlp/kernel"   ):
																				_A					  =			"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
																				_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/wo/kernel"   ) or key_name.endswith("/wi/kernel"   ):
																				_A					  =			key_name[-9:-7]
																				for i in range(16   ):
																								_A					  =			"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
																								_A					  =			(
																								    vnp[i].transpose([1, 0]   ).copy()
																								)  # In Mesh-Tensorflow, it is one array, so it is divided
																								_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/mlp"   ):
																_A					  =			int(key_name[9:].split("/"   )[0]   )
																if key_name.endswith("/p1/kernel"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.wi.weight" % player
																				_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/p1/bias"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.wi.bias" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/p2/kernel"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.wo.weight" % player
																				_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/p2/bias"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.wo.bias" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/ln"   ):
																_A					  =			int(key_name[8:].split("/"   )[0]   )
																if key_name.endswith("/b"   ):
																				_A					  =			"model.blocks.%d.feed_forward.norm.bias" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/g"   ):
																				_A					  =			"model.blocks.%d.feed_forward.norm.weight" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/att"   ):
																_A					  =			int(key_name[9:].split("/"   )[0]   )
																if key_name.endswith("/qkv/kernel"   ):
																				_A					  =			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																				_A					  =			state[:, 0, :, :]
																				_A					  =			state[:, 1, :, :]
																				_A					  =			state[:, 2, :, :]
																				_A					  =			(
																				    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]   )
																				    .transpose([1, 0]   )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			(
																				    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]   )
																				    .transpose([1, 0]   )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			(
																				    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]   )
																				    .transpose([1, 0]   )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
																				_A					  =			torch.tensor(__lowercase   )
																				_A					  =			"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
																				_A					  =			torch.tensor(__lowercase   )
																				_A					  =			"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/o/kernel"   ):
																				_A					  =			"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
																				_A					  =			(
																				    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]   ).transpose([1, 0]   ).copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/an"   ):
																_A					  =			int(key_name[8:].split("/"   )[0]   )
																if key_name.endswith("/b"   ):
																				_A					  =			"model.blocks.%d.self_attn.norm.bias" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/g"   ):
																				_A					  =			"model.blocks.%d.self_attn.norm.weight" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
												elif (
												    key_name.startswith("model/wte"   )
												    or key_name.startswith("model/wpe"   )
												    or key_name.startswith("model/ete"   )
												):
																_A					  =			{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
																    key_name[-3:]
																]
																_A					  =			"model.%s.weight" % nlayer
																_A					  =			vnp.copy()  # same in embedded
																_A					  =			torch.tensor(__lowercase   )
																if key_name.startswith("model/wte"   ):
																				_A					  =			"lm_head.weight"
																				_A					  =			vnp.copy()  # same in embedded
																				_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/wob"   ):
																_A					  =			"final_logits_bias"
																_A					  =			vnp.copy()  # same in embedded
																_A					  =			state.reshape((1, -1)   )
																_A					  =			torch.tensor(__lowercase   )
												elif key_name == "model/dense/kernel":
																_A					  =			"model.last_project.weight"
																_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_A					  =			torch.tensor(__lowercase   )
												elif key_name == "model/dense_1/bias":
																_A					  =			"model.last_project.bias"
																_A					  =			vnp.copy()  # same because it is one dimensional
																_A					  =			torch.tensor(__lowercase   )
				torch.save(__lowercase     ,			args.output   )


if __name__ == "__main__":
		a_       							=    argparse.ArgumentParser(
		    description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
		parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
		a_       							=    parser.parse_args()
		convert_tf_gptsan_to_pt(args)