





'''simple docstring'''

import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__		:     Dict			=							logging.get_logger(__name__)







def    a__							(							lowercase   :	List[str],   lowercase   :	List[Any],   lowercase   :	Union[str, Any] )						->						Optional[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			os.path.abspath(lowercase )
     logger.info(F"""Converting TensorFlow checkpoint from {tf_path}""" )
     # Load weights from TF model
     _UpperCamelCase							       =			tf.train.list_variables(lowercase )
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			[]
     for full_name, shape in init_vars:
          # logger.info(f"Loading TF weight {name} with shape {shape}")
          _UpperCamelCase							       =			full_name.split('''/''' )
          if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
               logger.info(F"""Skipping non-model layer {full_name}""" )
               continue
          if "optimizer" in full_name:
               logger.info(F"""Skipping optimization layer {full_name}""" )
               continue
          if name[0] == "model":
               # ignore initial 'model'
               _UpperCamelCase							       =			name[1:]
          # figure out how many levels deep the name is
          _UpperCamelCase							       =			0
          for _name in name:
               if _name.startswith('''layer_with_weights''' ):
                    depth += 1
               else:
                    break
          layer_depth.append(lowercase )
          # read data
          _UpperCamelCase							       =			tf.train.load_variable(lowercase,   lowercase )
          names.append('''/'''.join(lowercase ) )
          arrays.append(lowercase )
     logger.info(F"""Read a total of {len(lowercase ):,} layers""" )

     # Sanity check
     if len(set(lowercase ) ) != 1:
          raise ValueError(F"""Found layer names with different depths (layer depth {list(set(lowercase ) )})""" )
     _UpperCamelCase							       =			list(set(lowercase ) )[0]
     if layer_depth != 1:
          raise ValueError(
              '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
              ''' heads.''' )

     # convert layers
     logger.info('''Converting weights...''' )
     for full_name, array in zip(lowercase,   lowercase ):
          _UpperCamelCase							       =			full_name.split('''/''' )
          _UpperCamelCase							       =			model
          _UpperCamelCase							       =			[]
          for i, m_name in enumerate(lowercase ):
               if m_name == ".ATTRIBUTES":
                    # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                    break
               if m_name.startswith('''layer_with_weights''' ):
                    _UpperCamelCase							       =			int(m_name.split('''-''' )[-1] )
                    if layer_num <= 2:
                         # embedding layers
                         # layer_num 0: word_embeddings
                         # layer_num 1: position_embeddings
                         # layer_num 2: token_type_embeddings
                         continue
                    elif layer_num == 3:
                         # embedding LayerNorm
                         trace.extend(['''embeddings''', '''LayerNorm'''] )
                         _UpperCamelCase							       =			getattr(lowercase,   '''embeddings''' )
                         _UpperCamelCase							       =			getattr(lowercase,   '''LayerNorm''' )
                    elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                         # encoder layers
                         trace.extend(['''encoder''', '''layer''', str(layer_num - 4 )] )
                         _UpperCamelCase							       =			getattr(lowercase,   '''encoder''' )
                         _UpperCamelCase							       =			getattr(lowercase,   '''layer''' )
                         _UpperCamelCase							       =			pointer[layer_num - 4]
                    elif layer_num == config.num_hidden_layers + 4:
                         # pooler layer
                         trace.extend(['''pooler''', '''dense'''] )
                         _UpperCamelCase							       =			getattr(lowercase,   '''pooler''' )
                         _UpperCamelCase							       =			getattr(lowercase,   '''dense''' )
               elif m_name == "embeddings":
                    trace.append('''embeddings''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''embeddings''' )
                    if layer_num == 0:
                         trace.append('''word_embeddings''' )
                         _UpperCamelCase							       =			getattr(lowercase,   '''word_embeddings''' )
                    elif layer_num == 1:
                         trace.append('''position_embeddings''' )
                         _UpperCamelCase							       =			getattr(lowercase,   '''position_embeddings''' )
                    elif layer_num == 2:
                         trace.append('''token_type_embeddings''' )
                         _UpperCamelCase							       =			getattr(lowercase,   '''token_type_embeddings''' )
                    else:
                         raise ValueError(F"""Unknown embedding layer with name {full_name}""" )
                    trace.append('''weight''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''weight''' )
               elif m_name == "_attention_layer":
                    # self-attention layer
                    trace.extend(['''attention''', '''self'''] )
                    _UpperCamelCase							       =			getattr(lowercase,   '''attention''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''self''' )
               elif m_name == "_attention_layer_norm":
                    # output attention norm
                    trace.extend(['''attention''', '''output''', '''LayerNorm'''] )
                    _UpperCamelCase							       =			getattr(lowercase,   '''attention''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''output''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''LayerNorm''' )
               elif m_name == "_attention_output_dense":
                    # output attention dense
                    trace.extend(['''attention''', '''output''', '''dense'''] )
                    _UpperCamelCase							       =			getattr(lowercase,   '''attention''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''output''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''dense''' )
               elif m_name == "_output_dense":
                    # output dense
                    trace.extend(['''output''', '''dense'''] )
                    _UpperCamelCase							       =			getattr(lowercase,   '''output''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''dense''' )
               elif m_name == "_output_layer_norm":
                    # output dense
                    trace.extend(['''output''', '''LayerNorm'''] )
                    _UpperCamelCase							       =			getattr(lowercase,   '''output''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''LayerNorm''' )
               elif m_name == "_key_dense":
                    # attention key
                    trace.append('''key''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''key''' )
               elif m_name == "_query_dense":
                    # attention query
                    trace.append('''query''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''query''' )
               elif m_name == "_value_dense":
                    # attention value
                    trace.append('''value''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''value''' )
               elif m_name == "_intermediate_dense":
                    # attention intermediate dense
                    trace.extend(['''intermediate''', '''dense'''] )
                    _UpperCamelCase							       =			getattr(lowercase,   '''intermediate''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''dense''' )
               elif m_name == "_output_layer_norm":
                    # output layer norm
                    trace.append('''output''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''output''' )
               # weights & biases
               elif m_name in ["bias", "beta"]:
                    trace.append('''bias''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''bias''' )
               elif m_name in ["kernel", "gamma"]:
                    trace.append('''weight''' )
                    _UpperCamelCase							       =			getattr(lowercase,   '''weight''' )
               else:
                    logger.warning(F"""Ignored {m_name}""" )
        # for certain layers reshape is necessary
          _UpperCamelCase							       =			'''.'''.join(lowercase )
          if re.match(r'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)''',   lowercase ) or re.match(
              r'''(\S+)\.attention\.output\.dense\.weight''',   lowercase ):
               _UpperCamelCase							       =			array.reshape(pointer.data.shape )
          if "kernel" in full_name:
               _UpperCamelCase							       =			array.transpose()
          if pointer.shape == array.shape:
               _UpperCamelCase							       =			torch.from_numpy(lowercase )
          else:
               raise ValueError(
                   F"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
                   F""" {array.shape}""" )
          logger.info(F"""Successfully set variable {full_name} to PyTorch layer {trace}""" )
     return model







def    a__							(							lowercase   :	Union[str, Any],   lowercase   :	Union[str, Any],   lowercase   :	Optional[int] )						->						List[Any]:





     """simple docstring"""

     logger.info(F"""Loading model based on config from {config_path}...""" )
     _UpperCamelCase							       =			BertConfig.from_json_file(lowercase )
     _UpperCamelCase							       =			BertModel(lowercase )

     # Load weights from checkpoint
     logger.info(F"""Loading weights from checkpoint {tf_checkpoint_path}...""" )
     load_tfa_weights_in_bert(lowercase,   lowercase,   lowercase )

     # Save pytorch-model
     logger.info(F"""Saving PyTorch model to {pytorch_dump_path}...""" )
     torch.save(model.state_dict(),   lowercase )


if __name__ == "__main__":
   lowercase__		:     Tuple			=							argparse.ArgumentParser()
   parser.add_argument(
       '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x checkpoint path.'
   )
   parser.add_argument(
       '--bert_config_file',
       type=str,
       required=True,
       help='The config json file corresponding to the BERT model. This specifies the model architecture.',
   )
   parser.add_argument(
       '--pytorch_dump_path',
       type=str,
       required=True,
       help='Path to the output PyTorch model (must include filename).',
   )
   lowercase__		:     Union[str, Any]			=							parser.parse_args()
   convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









'''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      A       (						unittest.TestCase       ):







							def __init__(    self			:    Tuple				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    List[str]=7				,     lowerCAmelCase_			:    Dict=3				,     lowerCAmelCase_			:    List[Any]=18				,     lowerCAmelCase_			:    Any=30				,     lowerCAmelCase_			:    Optional[int]=4_00				,     lowerCAmelCase_			:    Union[str, Any]=True				,     lowerCAmelCase_			:    List[str]=None				,     lowerCAmelCase_			:    List[str]=True				,     )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							size if size is not None else {'''height''': 18, '''width''': 18}
														_a      	=							parent
														_a      	=							batch_size
														_a      	=							num_channels
														_a      	=							image_size
														_a      	=							min_resolution
														_a      	=							max_resolution
														_a      	=							do_resize
														_a      	=							size
														_a      	=							do_normalize







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														return {
														    # here we create 2 clusters for the sake of simplicity
														    "clusters": np.asarray(
														        [
														            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
														            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
														        ]   ),
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														}



@require_torch
@require_vision
class      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							ImageGPTImageProcessor if is_vision_available() else None







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


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessingTester(self   )







							@property
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				int:


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''clusters'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_resize'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''size'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_normalize'''   )   )







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


														"""simple docstring"""


														_a      	=							self.image_processing_class.from_dict(self.image_processor_dict   )
														self.assertEqual(image_processor.size				,     {'''height''': 18, '''width''': 18}   )

														_a      	=							self.image_processing_class.from_dict(self.image_processor_dict				,     size=42   )
														self.assertEqual(image_processor.size				,     {'''height''': 42, '''width''': 42}   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				str:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														_a      	=							json.loads(image_processor.to_json_string()   )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     obj[key]   )   )
																					else:
																												self.assertEqual(obj[key]				,     lowerCAmelCase_   )







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


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a      	=							os.path.join(lowerCAmelCase_				,     '''image_processor.json'''   )
																					image_processor_first.to_json_file(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_json_file(lowerCAmelCase_   ).to_dict()

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







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					image_processor_first.save_pretrained(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_pretrained(lowerCAmelCase_   ).to_dict()

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







							@unittest.skip('''ImageGPT requires clusters at initialization'''   )
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														pass



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							load_dataset('''hf-internal-testing/fixtures_image_utils'''					,		split='''test''' )

							_a      	=							Image.open(dataset[4]['''file'''] )
							_a      	=							Image.open(dataset[5]['''file'''] )

							_a      	=							[imagea, imagea]

							return images

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







							@slow
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''   )

														_a      	=							prepare_images()

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

														self.assertIsInstance(encoding.input_ids				,     torch.LongTensor   )
														self.assertEqual(encoding.input_ids.shape				,     (1, 10_24)   )

														_a      	=							[3_06, 1_91, 1_91]
														self.assertEqual(encoding.input_ids[0, :3].tolist()				,     lowerCAmelCase_   )

														# test batched
														_a      	=							image_processing(lowerCAmelCase_				,     return_tensors='''pt'''   )

														self.assertIsInstance(encoding.input_ids				,     torch.LongTensor   )
														self.assertEqual(encoding.input_ids.shape				,     (2, 10_24)   )

														_a      	=							[3_03, 13, 13]
														self.assertEqual(encoding.input_ids[1, -3:].tolist()				,     lowerCAmelCase_   )

