

'''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()
UpperCAmelCase					 =	logging.get_logger(__name__)

def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	Dict,       lowercase__    :	str			):






     '''simple docstring'''





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

     # Sanity check
     if len(set(UpperCAmelCase_			)			) != 1:
          raise ValueError(F'''Found layer names with different depths (layer depth {list(set(UpperCAmelCase_			)			)})'''			)
     __lowercase      		=list(set(UpperCAmelCase_			)			)[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(UpperCAmelCase_,       UpperCAmelCase_			):
          __lowercase      		=full_name.split('/'			)
          __lowercase      		=model
          __lowercase      		=[]
          for i, m_name in enumerate(UpperCAmelCase_			):
               if m_name == ".ATTRIBUTES":
                    # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                    break
               if m_name.startswith('layer_with_weights'			):
                    __lowercase      		=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']			)
                         __lowercase      		=getattr(UpperCAmelCase_,       'embeddings'			)
                         __lowercase      		=getattr(UpperCAmelCase_,       'LayerNorm'			)
                    elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                         # encoder layers
                         trace.extend(['encoder', 'layer', str(layer_num - 4			)]			)
                         __lowercase      		=getattr(UpperCAmelCase_,       'encoder'			)
                         __lowercase      		=getattr(UpperCAmelCase_,       'layer'			)
                         __lowercase      		=pointer[layer_num - 4]
                    elif layer_num == config.num_hidden_layers + 4:
                         # pooler layer
                         trace.extend(['pooler', 'dense']			)
                         __lowercase      		=getattr(UpperCAmelCase_,       'pooler'			)
                         __lowercase      		=getattr(UpperCAmelCase_,       'dense'			)
               elif m_name == "embeddings":
                    trace.append('embeddings'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'embeddings'			)
                    if layer_num == 0:
                         trace.append('word_embeddings'			)
                         __lowercase      		=getattr(UpperCAmelCase_,       'word_embeddings'			)
                    elif layer_num == 1:
                         trace.append('position_embeddings'			)
                         __lowercase      		=getattr(UpperCAmelCase_,       'position_embeddings'			)
                    elif layer_num == 2:
                         trace.append('token_type_embeddings'			)
                         __lowercase      		=getattr(UpperCAmelCase_,       'token_type_embeddings'			)
                    else:
                         raise ValueError(F'''Unknown embedding layer with name {full_name}'''			)
                    trace.append('weight'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'weight'			)
               elif m_name == "_attention_layer":
                    # self-attention layer
                    trace.extend(['attention', 'self']			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'attention'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'self'			)
               elif m_name == "_attention_layer_norm":
                    # output attention norm
                    trace.extend(['attention', 'output', 'LayerNorm']			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'attention'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'output'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'LayerNorm'			)
               elif m_name == "_attention_output_dense":
                    # output attention dense
                    trace.extend(['attention', 'output', 'dense']			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'attention'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'output'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'dense'			)
               elif m_name == "_output_dense":
                    # output dense
                    trace.extend(['output', 'dense']			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'output'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'dense'			)
               elif m_name == "_output_layer_norm":
                    # output dense
                    trace.extend(['output', 'LayerNorm']			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'output'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'LayerNorm'			)
               elif m_name == "_key_dense":
                    # attention key
                    trace.append('key'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'key'			)
               elif m_name == "_query_dense":
                    # attention query
                    trace.append('query'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'query'			)
               elif m_name == "_value_dense":
                    # attention value
                    trace.append('value'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'value'			)
               elif m_name == "_intermediate_dense":
                    # attention intermediate dense
                    trace.extend(['intermediate', 'dense']			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'intermediate'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'dense'			)
               elif m_name == "_output_layer_norm":
                    # output layer norm
                    trace.append('output'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'output'			)
               # weights & biases
               elif m_name in ["bias", "beta"]:
                    trace.append('bias'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'bias'			)
               elif m_name in ["kernel", "gamma"]:
                    trace.append('weight'			)
                    __lowercase      		=getattr(UpperCAmelCase_,       'weight'			)
               else:
                    logger.warning(F'''Ignored {m_name}'''			)
        # for certain layers reshape is necessary
          __lowercase      		='.'.join(UpperCAmelCase_			)
          if re.match(R'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)',       UpperCAmelCase_			) or re.match(
              R'(\S+)\.attention\.output\.dense\.weight',       UpperCAmelCase_			):
               __lowercase      		=array.reshape(pointer.data.shape			)
          if "kernel" in full_name:
               __lowercase      		=array.transpose()
          if pointer.shape == array.shape:
               __lowercase      		=torch.from_numpy(UpperCAmelCase_			)
          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 					__UpperCamelCase				(						lowercase__    :	Any,       lowercase__    :	Union[str, Any],       lowercase__    :	Any			):






     '''simple docstring'''





     logger.info(F'''Loading model based on config from {config_path}...'''			)
     __lowercase      		=BertConfig.from_json_file(UpperCAmelCase_			)
     __lowercase      		=BertModel(UpperCAmelCase_			)

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

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


if __name__ == "__main__":
       UpperCAmelCase					 =	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).''',
       )
       UpperCAmelCase					 =	parser.parse_args()
       convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







"""simple docstring"""







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Any:
				A__											=					tempfile.mkdtemp()

				A__											=					BlipImageProcessor()
				A__											=					GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"		)

				A__											=					BlipaProcessor(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				processor.save_pretrained(self.tmpdirname		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Dict:
				return AutoProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		).tokenizer


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				return AutoProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		).image_processor


			def   snake_case__	(      self		)      ->	str:
				shutil.rmtree(self.tmpdirname		)


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta		)]

				A__											=					[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__						,	0						,	-1		)		) for x in image_inputs]

				return image_inputs


			def   snake_case__	(      self		)      ->	str:
				A__											=					BlipaProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()		)
				processor.save_pretrained(self.tmpdirname		)

				A__											=					self.get_tokenizer(bos_token="(BOS)"						,	eos_token="(EOS)"		)
				A__											=					self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				A__											=					BlipaProcessor.from_pretrained(
				    self.tmpdirname						,	bos_token="(BOS)"						,	eos_token="(EOS)"						,	do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab()		)
				self.assertIsInstance(processor.tokenizer						,	SCREAMING_SNAKE_CASE__		)

				self.assertEqual(processor.image_processor.to_json_string()						,	image_processor_add_kwargs.to_json_string()		)
				self.assertIsInstance(processor.image_processor						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					self.prepare_image_inputs()

				A__											=					image_processor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)
				A__											=					processor(images=SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)

				for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()						,	input_processor[key].sum()						,	delta=1e-2		)


			def   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"

				A__											=					processor(text=SCREAMING_SNAKE_CASE__		)

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	return_token_type_ids=SCREAMING_SNAKE_CASE__		)

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]						,	encoded_processor[key]		)


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(list(inputs.keys()		)						,	["pixel_values", "input_ids", "attention_mask"]		)

				# test if it raises when no input is passed
				with pytest.raises(SCREAMING_SNAKE_CASE__		):
					processor()


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				A__											=					processor.batch_decode(SCREAMING_SNAKE_CASE__		)
				A__											=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

				# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
				self.assertListEqual(list(inputs.keys()		)						,	["pixel_values", "input_ids", "attention_mask"]		)




