




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a      =							{
    'configuration_blenderbot_small': [
        'BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotSmallConfig',
        'BlenderbotSmallOnnxConfig',
    ],
    'tokenization_blenderbot_small': ['BlenderbotSmallTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							['BlenderbotSmallTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BlenderbotSmallForCausalLM',
		    'BlenderbotSmallForConditionalGeneration',
		    'BlenderbotSmallModel',
		    'BlenderbotSmallPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'TFBlenderbotSmallForConditionalGeneration',
		    'TFBlenderbotSmallModel',
		    'TFBlenderbotSmallPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'FlaxBlenderbotSmallForConditionalGeneration',
		    'FlaxBlenderbotSmallModel',
		    'FlaxBlenderbotSmallPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_blenderbot_small import (
		    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlenderbotSmallConfig,
		    BlenderbotSmallOnnxConfig,
		)
		from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blenderbot_small import (
				    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlenderbotSmallForCausalLM,
				    BlenderbotSmallForConditionalGeneration,
				    BlenderbotSmallModel,
				    BlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blenderbot_small import (
				    TFBlenderbotSmallForConditionalGeneration,
				    TFBlenderbotSmallModel,
				    TFBlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    FlaxBlenderbotSmallPreTrainedModel,
				)

else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











"""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()
a      =							logging.get_logger(__name__)





def 							lowercase					(snake_case__   : str      ,       snake_case__   : Tuple      ,       snake_case__   : Dict )     ->  int:


				'''simple docstring'''






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

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


				'''simple docstring'''






				logger.info(f'''Loading model based on config from {config_path}...''' )
				lowerCAmelCase  =				BertConfig.from_json_file(snake_case__ )
				lowerCAmelCase  =				BertModel(snake_case__ )

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

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


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





