





"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]					) ->str:






	'''simple docstring'''






	a      :		Union[str, Any]	=				os.path.join(args.tf_model_dir    ,       "parameters.json"					)
	a      :		str	=				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      :		str	=				args.output + ".pt"
	a      :		Dict	=				OrderedDict()
	with tf.device("/CPU:0"					):
		a      :		Optional[int]	=				tf.train.load_checkpoint(args.tf_model_dir					)
		a      :		Optional[Any]	=				reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			a      :		Dict	=				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      :		Union[str, Any]	=				int(key_name[9]					)
				elif key_name.startswith("pasts/out"					):
					a      :		Optional[int]	=				8
				a      :		Dict	=				"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				a      :		int	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/moe"					):
				a      :		List[str]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/switch_gating/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[str]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/softmlp/kernel"					):
					a      :		Optional[int]	=				"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/wo/kernel"					) or key_name.endswith("/wi/kernel"					):
					a      :		Any	=				key_name[-9:-7]
					for i in range(16					):
						a      :		List[Any]	=				"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
						a      :		str	=				(
						    vnp[i].transpose([1, 0]					).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/mlp"					):
				a      :		Union[str, Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/p1/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.weight" % player
					a      :		Dict	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p1/bias"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.bias" % player
					a      :		List[Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/kernel"					):
					a      :		Union[str, Any]	=				"model.blocks.%d.feed_forward.mlp.wo.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/bias"					):
					a      :		Dict	=				"model.blocks.%d.feed_forward.mlp.wo.bias" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		str	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/ln"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.bias" % player
					a      :		Tuple	=				vnp.copy()  # same because it is one dimensional
					a      :		int	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.weight" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/att"					):
				a      :		Optional[Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/qkv/kernel"					):
					a      :		Union[str, Any]	=				vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					a      :		List[str]	=				state[:, 0, :, :]
					a      :		Dict	=				state[:, 1, :, :]
					a      :		Union[str, Any]	=				state[:, 2, :, :]
					a      :		str	=				(
					    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      :		List[str]	=				(
					    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      :		Dict	=				(
					    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      :		List[Any]	=				"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
					a      :		Union[str, Any]	=				torch.tensor(_lowercase					)
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
					a      :		List[str]	=				torch.tensor(_lowercase					)
					a      :		Optional[Any]	=				"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
					a      :		Optional[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/o/kernel"					):
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
					a      :		Optional[int]	=				(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]					).transpose([1, 0]					).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/an"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[int]	=				"model.blocks.%d.self_attn.norm.bias" % player
					a      :		Union[str, Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Any	=				"model.blocks.%d.self_attn.norm.weight" % player
					a      :		str	=				vnp.copy()  # same because it is one dimensional
					a      :		Any	=				torch.tensor(_lowercase					)
			elif (
			    key_name.startswith("model/wte"					)
			    or key_name.startswith("model/wpe"					)
			    or key_name.startswith("model/ete"					)
			):
				a      :		Optional[int]	=				{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
				    key_name[-3:]
				]
				a      :		Tuple	=				"model.%s.weight" % nlayer
				a      :		Any	=				vnp.copy()  # same in embedded
				a      :		Tuple	=				torch.tensor(_lowercase					)
				if key_name.startswith("model/wte"					):
					a      :		Optional[int]	=				"lm_head.weight"
					a      :		Optional[int]	=				vnp.copy()  # same in embedded
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/wob"					):
				a      :		Optional[int]	=				"final_logits_bias"
				a      :		Optional[Any]	=				vnp.copy()  # same in embedded
				a      :		Optional[int]	=				state.reshape((1, -1)					)
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense/kernel":
				a      :		Optional[int]	=				"model.last_project.weight"
				a      :		Union[str, Any]	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense_1/bias":
				a      :		Dict	=				"model.last_project.bias"
				a      :		Optional[Any]	=				vnp.copy()  # same because it is one dimensional
				a      :		Any	=				torch.tensor(_lowercase					)
	torch.save(_lowercase    ,       args.output					)


if __name__ == "__main__":
		a						: Optional[int]     	=			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						: Tuple     	=			parser.parse_args()
		convert_tf_gptsan_to_pt(args)






'''simple docstring'''



from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
	from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  self.config_class(**self.inputs_dict							)
						self.parent.assertTrue(hasattr(__a						,				'embed_dim'							)							)
						self.parent.assertTrue(hasattr(__a						,				'num_heads'							)							)





class 							__UpperCamelCase				:




	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-1_2						,				__a=True						,				__a=True						,				__a=2						,				):

						'''simple docstring'''





						__a			:       str					    =  parent
						__a			:       List[Any]					    =  batch_size
						__a			:       Optional[int]					    =  image_size
						__a			:       List[str]					    =  patch_sizes
						__a			:       str					    =  patch_stride
						__a			:       Any					    =  patch_padding
						__a			:       Dict					    =  is_training
						__a			:       Union[str, Any]					    =  use_labels
						__a			:       Dict					    =  num_labels
						__a			:       List[Any]					    =  num_channels
						__a			:       Any					    =  embed_dim
						__a			:       int					    =  num_heads
						__a			:       Optional[int]					    =  stride_kv
						__a			:       Dict					    =  depth
						__a			:       List[str]					    =  cls_token
						__a			:       List[Any]					    =  attention_drop_rate
						__a			:       Tuple					    =  initializer_range
						__a			:       int					    =  layer_norm_eps




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						__a			:       Dict					    =  None
						if self.use_labels:
											# create a random int32 tensor of given shape
											__a			:       str					    =  ids_tensor([self.batch_size]						,				self.num_labels							)

						__a			:       str					    =  self.get_config()
						return config, pixel_values, labels




	def       __UpperCAmelCase							(   self							):

						'''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							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  TFCvtModel(config=__a							)
						__a			:       Dict					    =  model(__a						,				training=__a							)
						__a			:       Any					    =  (self.image_size, self.image_size)
						__a    ,				__a			:       Dict					    =  image_size[0], image_size[1]
						for i in range(len(self.depth							)							):
											__a			:       Tuple					    =  floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1							)
											__a			:       str					    =  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							):

						'''simple docstring'''





						__a			:       List[Any]					    =  self.num_labels
						__a			:       Optional[int]					    =  TFCvtForImageClassification(__a							)
						__a			:       Dict					    =  model(__a						,				labels=__a						,				training=__a							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_labels)							)





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  self.prepare_config_and_inputs()
						__a    ,				__a    ,				__a			:       Tuple					    =  config_and_inputs
						__a			:       str					    =  {'pixel_values': pixel_values}
						return config, inputs_dict





@require_tf
class 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
	A_				=    (
	    {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
	    if is_tf_available()
	    else {}
	)
	A_				=    False
	A_				=    False
	A_				=    False
	A_				=    False
	A_				=    False




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  TFCvtModelTester(self							)
						__a			:       List[Any]					    =  TFCvtConfigTester(self						,				config_class=__a						,				has_text_modality=__a						,				hidden_size=37							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.config_tester.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()




	@unittest.skip(reason='Cvt does not output attentions'							)
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						pass




	@unittest.skip(reason='Cvt does not use inputs_embeds'							)
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						pass




	@unittest.skip(reason='Cvt does not support input and output embeddings'							)
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						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.'						,				)
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().test_dataset_conversion()




	@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							):

						'''simple docstring'''





						super().test_keras_fit()




	@unittest.skip(reason='Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'							)
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Any					    =  tf.keras.mixed_precision.Policy('mixed_float16'							)
						tf.keras.mixed_precision.set_global_policy(__a							)
						super().test_keras_fit()
						tf.keras.mixed_precision.set_global_policy('float32'							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a    ,				__a			:       List[Any]					    =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__a			:       Dict					    =  model_class(__a							)
											__a			:       Optional[Any]					    =  inspect.signature(model.call							)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__a			:       Optional[Any]					    =  [*signature.parameters.keys()]

											__a			:       Optional[Any]					    =  ['pixel_values']
											self.assertListEqual(arg_names[:1]						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						def check_hidden_states_output(__a						,				__a						,				__a							):
											__a			:       List[str]					    =  model_class(__a							)

											__a			:       Union[str, Any]					    =  model(**self._prepare_for_class(__a						,				__a							)							)
											__a			:       Any					    =  outputs.hidden_states

											__a			:       Union[str, Any]					    =  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,
											    ]						,				)

						__a    ,				__a			:       Optional[int]					    =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__a			:       List[str]					    =  True
											check_hidden_states_output(__a						,				__a						,				__a							)

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

											check_hidden_states_output(__a						,				__a						,				__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__a							)





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__a			:       Optional[Any]					    =  TFCvtModel.from_pretrained(__a							)
											self.assertIsNotNone(__a							)






def   lowerCamelCase							():
					__a			:       List[Any]					    =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
					return image





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




	@cached_property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

						__a			:       Tuple					    =  self.default_image_processor
						__a			:       Any					    =  prepare_img()
						__a			:       int					    =  image_processor(images=__a						,				return_tensors='tf'							)

						# forward pass
						__a			:       Any					    =  model(**__a							)

						# verify the logits
						__a			:       Any					    =  tf.TensorShape((1, 1000)							)
						self.assertEqual(outputs.logits.shape						,				__a							)

						__a			:       Optional[Any]					    =  tf.constant([0.9285, 0.9015, -0.3150]							)
						self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()						,				__a						,				atol=1E-4							)							)



