


'''simple docstring'''






import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:
							class 						__UpperCamelCase	:





														@staticmethod
														def 		a__   (  *_UpperCamelCase		:Union[str, Any]  ,**_UpperCamelCase		:str			):
																pass
@is_pipeline_test
@require_torch
@require_vision
class 						__UpperCamelCase	(						unittest.TestCase							):
							lowercase			:		Tuple   		=  MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING





							def 		a__   (  self		:Any  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Tuple			):
									snake_case_		:							List[Any]			       =						pipeline("""visual-question-answering"""  ,model="""hf-internal-testing/tiny-vilt-random-vqa"""			)
									snake_case_		:							List[str]			       =						[
									    {
									        """image""": Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			),
									        """question""": """How many cats are there?""",
									    },
									    {
									        """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
									        """question""": """How many cats are there?""",
									    },
									]
									return vqa_pipeline, examples





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Any			):
									snake_case_		:							Optional[Any]			       =						vqa_pipeline(_UpperCamelCase  ,top_k=1			)
									self.assertEqual(
									    _UpperCamelCase  ,[
									        [{"""score""": ANY(_UpperCamelCase			), """answer""": ANY(_UpperCamelCase			)}],
									        [{"""score""": ANY(_UpperCamelCase			), """answer""": ANY(_UpperCamelCase			)}],
									    ]  ,)





							@require_torch
							def 		a__   (  self		:Any			):
									snake_case_		:							Optional[Any]			       =						pipeline("""visual-question-answering"""  ,model="""hf-internal-testing/tiny-vilt-random-vqa"""			)
									snake_case_		:							List[Any]			       =						"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
									snake_case_		:							Tuple			       =						"""How many cats are there?"""

									snake_case_		:							int			       =						vqa_pipeline(image=_UpperCamelCase  ,question="""How many cats are there?"""  ,top_k=2			)
									self.assertEqual(
									    _UpperCamelCase  ,[{"""score""": ANY(_UpperCamelCase			), """answer""": ANY(_UpperCamelCase			)}, {"""score""": ANY(_UpperCamelCase			), """answer""": ANY(_UpperCamelCase			)}]			)

									snake_case_		:							List[str]			       =						vqa_pipeline({"""image""": image, """question""": question}  ,top_k=2			)
									self.assertEqual(
									    _UpperCamelCase  ,[{"""score""": ANY(_UpperCamelCase			), """answer""": ANY(_UpperCamelCase			)}, {"""score""": ANY(_UpperCamelCase			), """answer""": ANY(_UpperCamelCase			)}]			)





							@slow
							@require_torch
							def 		a__   (  self		:Dict			):
									snake_case_		:							Dict			       =						pipeline("""visual-question-answering"""  ,model="""dandelin/vilt-b32-finetuned-vqa"""			)
									snake_case_		:							List[Any]			       =						"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
									snake_case_		:							Dict			       =						"""How many cats are there?"""

									snake_case_		:							Union[str, Any]			       =						vqa_pipeline(image=_UpperCamelCase  ,question=_UpperCamelCase  ,top_k=2			)
									self.assertEqual(
									    nested_simplify(_UpperCamelCase  ,decimals=4			)  ,[{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]			)

									snake_case_		:							Optional[int]			       =						vqa_pipeline({"""image""": image, """question""": question}  ,top_k=2			)
									self.assertEqual(
									    nested_simplify(_UpperCamelCase  ,decimals=4			)  ,[{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]			)

									snake_case_		:							Optional[int]			       =						vqa_pipeline(
									    [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]  ,top_k=2			)
									self.assertEqual(
									    nested_simplify(_UpperCamelCase  ,decimals=4			)  ,[[{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]] * 2  ,)





							@require_tf
							@unittest.skip("""Visual question answering not implemented in TF"""			)
							def 		a__   (  self		:Dict			):
									pass






'''simple docstring'''






# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :List[Any]=0 ):







		'''simple docstring'''
		# Format the message.
		if name is None:
				snake_case_		:							Tuple			       =						None
		else:
				snake_case_		:							Optional[Any]			       =						""".""" * max(0				,      spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
				snake_case_		:							Optional[Any]			       =						fmt.format(lowerCamelCase_ )

		# Print and recurse (if needed).
		if isinstance(lowerCamelCase_				,      lowerCamelCase_ ):
				if msg is not None:
						print(lowerCamelCase_ )
				for k in val.keys():
						recursive_print(lowerCamelCase_				,      val[k]				,      spaces + 2 )
		elif isinstance(lowerCamelCase_				,      torch.Tensor ):
				print(lowerCamelCase_				,      """:"""				,      val.size() )
		else:
				print(lowerCamelCase_				,      """:"""				,      lowerCamelCase_ )





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[int]				,      lowerCamelCase_  :List[str]				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[str]				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
		# for compatibility with later versions of NVIDIA Megatron-LM.
		# The inverse operation is performed inside Megatron-LM to read checkpoints:
		# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
		# If param is the weight tensor of the self-attention block, the returned tensor
		# will have to be transposed one more time to be read by HuggingFace GPT2.
		snake_case_		:							Any			       =						param.size()
		if checkpoint_version == 1.0:
				# version 1.0 stores [num_heads * hidden_size * num_splits, :]
				snake_case_		:							List[str]			       =						(num_heads, hidden_size, num_splits) + input_shape[1:]
				snake_case_		:							List[Any]			       =						param.view(*lowerCamelCase_ )
				snake_case_		:							Tuple			       =						param.transpose(0				,      2 )
				snake_case_		:							List[str]			       =						param.transpose(1				,      2 ).contiguous()
		elif checkpoint_version >= 2.0:
				# other versions store [num_heads * num_splits * hidden_size, :]
				snake_case_		:							Tuple			       =						(num_heads, num_splits, hidden_size) + input_shape[1:]
				snake_case_		:							int			       =						param.view(*lowerCamelCase_ )
				snake_case_		:							str			       =						param.transpose(0				,      1 ).contiguous()
		snake_case_		:							int			       =						param.view(*lowerCamelCase_ )
		return param





def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Tuple ):







		'''simple docstring'''
		# The converted output model.
		snake_case_		:							Tuple			       =						{}

		# old versions did not store training args
		snake_case_		:							Optional[Any]			       =						input_state_dict.get("""args"""				,      lowerCamelCase_ )
		if ds_args is not None:
				# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
				# from pprint import pprint
				# pprint(vars(ds_args))

				snake_case_		:							Optional[int]			       =						ds_args.padded_vocab_size
				snake_case_		:							str			       =						ds_args.max_position_embeddings
				snake_case_		:							Tuple			       =						ds_args.hidden_size
				snake_case_		:							List[str]			       =						ds_args.num_layers
				snake_case_		:							Union[str, Any]			       =						ds_args.num_attention_heads
				snake_case_		:							Tuple			       =						ds_args.ffn_hidden_size
				# pprint(config)

		# The number of heads.
		snake_case_		:							int			       =						config.n_head
		# The hidden_size per head.
		snake_case_		:							Any			       =						config.n_embd // config.n_head
		# Megatron-LM checkpoint version
		if "checkpoint_version" in input_state_dict.keys():
				snake_case_		:							Tuple			       =						input_state_dict["""checkpoint_version"""]
		else:
				snake_case_		:							Dict			       =						0.0

		# The model.
		snake_case_		:							Optional[Any]			       =						input_state_dict["""model"""]
		# The language model.
		snake_case_		:							Optional[Any]			       =						model["""language_model"""]
		# The embeddings.
		snake_case_		:							int			       =						lm["""embedding"""]

		# The word embeddings.
		snake_case_		:							Any			       =						embeddings["""word_embeddings"""]["""weight"""]
		# Truncate the embedding table to vocab_size rows.
		snake_case_		:							Any			       =						word_embeddings[: config.vocab_size, :]
		snake_case_		:							Union[str, Any]			       =						word_embeddings

		# The position embeddings.
		snake_case_		:							Tuple			       =						embeddings["""position_embeddings"""]["""weight"""]
		# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
		snake_case_		:							List[str]			       =						pos_embeddings.size(0 )
		if n_positions != config.n_positions:
				raise ValueError(
				    F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
		# Store the position embeddings.
		snake_case_		:							List[str]			       =						pos_embeddings

		# The transformer.
		snake_case_		:							str			       =						lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

		# The regex to extract layer names.
		snake_case_		:							Union[str, Any]			       =						re.compile(R"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )

		# The simple map of names for "automated" rules.
		snake_case_		:							Tuple			       =						{
		    """attention.dense""": """.attn.c_proj.""",
		    """self_attention.dense""": """.attn.c_proj.""",
		    """mlp.dense_h_to_4h""": """.mlp.c_fc.""",
		    """mlp.dense_4h_to_h""": """.mlp.c_proj.""",
		}

		# Extract the layers.
		for key, val in transformer.items():
				# Match the name.
				snake_case_		:							List[Any]			       =						layer_re.match(lowerCamelCase_ )

				# Stop if that's not a layer
				if m is None:
						break

				# The index of the layer.
				snake_case_		:							Any			       =						int(m.group(1 ) )
				# The name of the operation.
				snake_case_		:							Any			       =						m.group(2 )
				# Is it a weight or a bias?
				snake_case_		:							List[str]			       =						m.group(3 )

				# The name of the layer.
				snake_case_		:							List[Any]			       =						F'''transformer.h.{layer_idx}'''

				# For layernorm(s), simply store the layer norm.
				if op_name.endswith("""layernorm""" ):
						snake_case_		:							str			       =						"""ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
						snake_case_		:							Optional[int]			       =						val

				# Transpose the QKV matrix.
				elif (
				    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
				) and weight_or_bias == "weight":
						# Insert a tensor of 1x1xDxD bias.
						snake_case_		:							int			       =						torch.tril(torch.ones((n_positions, n_positions)				,      dtype=torch.floataa ) ).view(
						    1				,      1				,      lowerCamelCase_				,      lowerCamelCase_ )
						snake_case_		:							Any			       =						causal_mask

						# Insert a "dummy" tensor for masked_bias.
						snake_case_		:							List[str]			       =						torch.tensor(-1E4				,      dtype=torch.floataa )
						snake_case_		:							Optional[Any]			       =						masked_bias

						snake_case_		:							Optional[Any]			       =						fix_query_key_value_ordering(lowerCamelCase_				,      lowerCamelCase_				,      3				,      lowerCamelCase_				,      lowerCamelCase_ )
						# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
						snake_case_		:							Any			       =						out_val.transpose(0				,      1 ).contiguous()
						# Store.
						snake_case_		:							int			       =						out_val

				# Transpose the bias.
				elif (
				    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
				) and weight_or_bias == "bias":
						snake_case_		:							Dict			       =						fix_query_key_value_ordering(lowerCamelCase_				,      lowerCamelCase_				,      3				,      lowerCamelCase_				,      lowerCamelCase_ )
						# Store. No change of shape.
						snake_case_		:							Tuple			       =						out_val

				# Transpose the weights.
				elif weight_or_bias == "weight":
						snake_case_		:							str			       =						megatron_to_transformers[op_name]
						snake_case_		:							Union[str, Any]			       =						val.transpose(0				,      1 )

				# Copy the bias.
				elif weight_or_bias == "bias":
						snake_case_		:							str			       =						megatron_to_transformers[op_name]
						snake_case_		:							List[str]			       =						val

    # DEBUG.
		assert config.n_layer == layer_idx + 1

		# The final layernorm.
		snake_case_		:							str			       =						transformer["""final_layernorm.weight"""]
		snake_case_		:							int			       =						transformer["""final_layernorm.bias"""]

		# For LM head, transformers' wants the matrix to weight embeddings.
		snake_case_		:							Optional[int]			       =						word_embeddings

		# It should be done!
		return output_state_dict





def      UpperCAmelCase      (		):







		'''simple docstring'''
		# Create the argument parser.
		snake_case_		:							str			       =						argparse.ArgumentParser()
		parser.add_argument("""--print-checkpoint-structure"""				,      action="""store_true""" )
		parser.add_argument(
		    """path_to_checkpoint"""				,      type=lowerCamelCase_				,      help="""Path to the checkpoint file (.zip archive or direct .pt file)"""				,      )
		parser.add_argument(
		    """--config_file"""				,      default=""""""				,      type=lowerCamelCase_				,      help="""An optional config json file describing the pre-trained model."""				,      )
		snake_case_		:							Tuple			       =						parser.parse_args()

		# Extract the basename.
		snake_case_		:							List[str]			       =						os.path.dirname(args.path_to_checkpoint )

		# Load the model.
		# the .zip is very optional, let's keep it for backward compatibility
		print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
		if args.path_to_checkpoint.endswith(""".zip""" ):
				with zipfile.ZipFile(args.path_to_checkpoint				,      """r""" ) as checkpoint:
						with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
								snake_case_		:							Dict			       =						torch.load(lowerCamelCase_				,      map_location="""cpu""" )
		else:
				snake_case_		:							Optional[Any]			       =						torch.load(args.path_to_checkpoint				,      map_location="""cpu""" )

		snake_case_		:							Optional[int]			       =						input_state_dict.get("""args"""				,      lowerCamelCase_ )

		# Read the config, or default to the model released by NVIDIA.
		if args.config_file == "":
				if ds_args is not None:
						if ds_args.bias_gelu_fusion:
								snake_case_		:							Tuple			       =						"""gelu_fast"""
						elif ds_args.openai_gelu:
								snake_case_		:							int			       =						"""gelu_new"""
						else:
								snake_case_		:							Optional[Any]			       =						"""gelu"""
				else:
						# in the very early days this used to be "gelu_new"
						snake_case_		:							int			       =						"""gelu_new"""

				# Spell out all parameters in case the defaults change.
				snake_case_		:							int			       =						GPTaConfig(
				    vocab_size=5_02_57				,      n_positions=10_24				,      n_embd=10_24				,      n_layer=24				,      n_head=16				,      n_inner=40_96				,      activation_function=lowerCamelCase_				,      resid_pdrop=0.1				,      embd_pdrop=0.1				,      attn_pdrop=0.1				,      layer_norm_epsilon=1E-5				,      initializer_range=0.02				,      summary_type="""cls_index"""				,      summary_use_proj=lowerCamelCase_				,      summary_activation=lowerCamelCase_				,      summary_proj_to_labels=lowerCamelCase_				,      summary_first_dropout=0.1				,      scale_attn_weights=lowerCamelCase_				,      use_cache=lowerCamelCase_				,      bos_token_id=5_02_56				,      eos_token_id=5_02_56				,      )
		else:
				snake_case_		:							int			       =						GPTaConfig.from_json_file(args.config_file )

		snake_case_		:							Optional[int]			       =						["""GPT2LMHeadModel"""]

		# Convert.
		print("""Converting""" )
		snake_case_		:							Optional[Any]			       =						convert_megatron_checkpoint(lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ )

		# Print the structure of converted state dict.
		if args.print_checkpoint_structure:
				recursive_print(lowerCamelCase_				,      lowerCamelCase_ )

		# Add tokenizer class info to config
		# see https://github.com/huggingface/transformers/issues/13906)
		if ds_args is not None:
				snake_case_		:							str			       =						ds_args.tokenizer_type
				if tokenizer_type == "GPT2BPETokenizer":
						snake_case_		:							List[str]			       =						"""gpt2"""
				elif tokenizer_type == "PretrainedFromHF":
						snake_case_		:							str			       =						ds_args.tokenizer_name_or_path
				else:
						raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
		else:
				snake_case_		:							List[str]			       =						"""gpt2"""

		snake_case_		:							Union[str, Any]			       =						AutoTokenizer.from_pretrained(lowerCamelCase_ )
		snake_case_		:							int			       =						type(lowerCamelCase_ ).__name__
		snake_case_		:							Union[str, Any]			       =						tokenizer_class

		# Store the config to file.
		print("""Saving config""" )
		config.save_pretrained(lowerCamelCase_ )

		# Save tokenizer based on args
		print(F'''Adding {tokenizer_class} tokenizer files''' )
		tokenizer.save_pretrained(lowerCamelCase_ )

		# Store the state_dict to file.
		snake_case_		:							Optional[Any]			       =						os.path.join(lowerCamelCase_				,      """pytorch_model.bin""" )
		print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
		torch.save(lowerCamelCase_				,      lowerCamelCase_ )


####################################################################################################

if __name__ == "__main__":
							main()

####################################################################################################