

'''simple docstring'''





import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ImageGPTImageProcessor





class 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







				def __init__(					self    :		str		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		int=7		,				UpperCAmelCase__    :		Optional[Any]=3		,				UpperCAmelCase__    :		Optional[int]=18		,				UpperCAmelCase__    :		List[Any]=30		,				UpperCAmelCase__    :		Union[str, Any]=400		,				UpperCAmelCase__    :		Union[str, Any]=True		,				UpperCAmelCase__    :		Any=None		,				UpperCAmelCase__    :		Union[str, Any]=True		,				):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=size if size is not None else {"height": 18, "width": 18}
										lowercase   :       Optional[int]								=parent
										lowercase   :       Dict								=batch_size
										lowercase   :       Optional[Any]								=num_channels
										lowercase   :       List[str]								=image_size
										lowercase   :       Optional[int]								=min_resolution
										lowercase   :       str								=max_resolution
										lowercase   :       Dict								=do_resize
										lowercase   :       Optional[int]								=size
										lowercase   :       List[str]								=do_normalize







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										return {
										    # here we create 2 clusters for the sake of simplicity
										    "clusters": np.asarray(
										        [
										            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
										            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
										        ]      ),
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										}





@require_torch
@require_vision
class 		__SCREAMING_SNAKE_CASE       ( _snake_case	,       unittest.TestCase			):
				lowerCamelCase_							=						ImageGPTImageProcessor if is_vision_available() else None







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       List[str]								=ImageGPTImageProcessingTester(self      )







				@property
				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.image_processing_class(**self.image_processor_dict      )
										self.assertTrue(hasattr(snake_case_		,				'''clusters'''      )      )
										self.assertTrue(hasattr(snake_case_		,				'''do_resize'''      )      )
										self.assertTrue(hasattr(snake_case_		,				'''size'''      )      )
										self.assertTrue(hasattr(snake_case_		,				'''do_normalize'''      )      )







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=self.image_processing_class.from_dict(self.image_processor_dict      )
										self.assertEqual(image_processor.size		,				{'''height''': 18, '''width''': 18}      )

										lowercase   :       Union[str, Any]								=self.image_processing_class.from_dict(self.image_processor_dict		,				size=42      )
										self.assertEqual(image_processor.size		,				{'''height''': 42, '''width''': 42}      )







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Dict								=self.image_processing_class(**self.image_processor_dict      )
										lowercase   :       Dict								=json.loads(image_processor.to_json_string()      )
										for key, value in self.image_processor_dict.items():
																if key == "clusters":
																						self.assertTrue(np.array_equal(snake_case_		,				obj[key]      )      )
																else:
																						self.assertEqual(obj[key]		,				snake_case_      )







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.image_processing_class(**self.image_processor_dict      )

										with tempfile.TemporaryDirectory() as tmpdirname:
																lowercase   :       List[Any]								=os.path.join(snake_case_		,				'''image_processor.json'''      )
																image_processor_first.to_json_file(snake_case_      )
																lowercase   :       str								=self.image_processing_class.from_json_file(snake_case_      ).to_dict()

										lowercase   :       Optional[int]								=image_processor_first.to_dict()
										for key, value in image_processor_first.items():
																if key == "clusters":
																						self.assertTrue(np.array_equal(snake_case_		,				image_processor_second[key]      )      )
																else:
																						self.assertEqual(image_processor_first[key]		,				snake_case_      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Tuple								=self.image_processing_class(**self.image_processor_dict      )

										with tempfile.TemporaryDirectory() as tmpdirname:
																image_processor_first.save_pretrained(snake_case_      )
																lowercase   :       Any								=self.image_processing_class.from_pretrained(snake_case_      ).to_dict()

										lowercase   :       List[str]								=image_processor_first.to_dict()
										for key, value in image_processor_first.items():
																if key == "clusters":
																						self.assertTrue(np.array_equal(snake_case_		,				image_processor_second[key]      )      )
																else:
																						self.assertEqual(image_processor_first[key]		,				snake_case_      )







				@unittest.skip('''ImageGPT requires clusters at initialization'''      )
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										pass






def 							_lowerCAmelCase (      )							->     Optional[Any]:

						'''simple docstring'''





						lowercase   :       Optional[int]								=load_dataset('''hf-internal-testing/fixtures_image_utils'''      ,   split='''test'''	)

						lowercase   :       List[str]								=Image.open(dataset[4]['''file''']	)
						lowercase   :       List[str]								=Image.open(dataset[5]['''file''']	)

						lowercase   :       Union[str, Any]								=[imagea, imagea]

						return images







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







				@slow
				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''      )

										lowercase   :       Any								=prepare_images()

										# test non-batched
										lowercase   :       int								=image_processing(images[0]		,				return_tensors='''pt'''      )

										self.assertIsInstance(encoding.input_ids		,				torch.LongTensor      )
										self.assertEqual(encoding.input_ids.shape		,				(1, 1024)      )

										lowercase   :       Optional[Any]								=[306, 191, 191]
										self.assertEqual(encoding.input_ids[0, :3].tolist()		,				snake_case_      )

										# test batched
										lowercase   :       Dict								=image_processing(snake_case_		,				return_tensors='''pt'''      )

										self.assertIsInstance(encoding.input_ids		,				torch.LongTensor      )
										self.assertEqual(encoding.input_ids.shape		,				(2, 1024)      )

										lowercase   :       Any								=[303, 13, 13]
										self.assertEqual(encoding.input_ids[1, -3:].tolist()		,				snake_case_      )








'''simple docstring'''





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






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


if __name__ == "__main__":
							UpperCamelCase_							=					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""")
							UpperCamelCase_							=					parser.parse_args()
							convert_tf_gptsan_to_pt(args)





