


'''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    (    _A    :				Optional[Any]					,   _A    :				Tuple					,   _A    :				str=0				)    ->      Optional[int]:




	"""simple docstring"""





	if name is None:
		lowerCAmelCase					:    Optional[int]      							= None
	else:
		lowerCAmelCase					:    Any      							= '.' * max(0					,   spaces - 2				) + '# {:' + str(50 - spaces				) + 's}'
		lowerCAmelCase					:    Dict      							= fmt.format(_A				)

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

def 			__UpperCamelCase    (    _A    :				List[str]					,   _A    :				Tuple					,   _A    :				List[str]					,   _A    :				Optional[int]					,   _A    :				List[Any]				)    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= param.size()
	if checkpoint_version == 1.0:
		# version 1.0 stores [num_heads * hidden_size * num_splits, :]
		lowerCAmelCase					:    Tuple      							= (num_heads, hidden_size, num_splits) + input_shape[1:]
		lowerCAmelCase					:    Tuple      							= param.view(*_A				)
		lowerCAmelCase					:    Any      							= param.transpose(0					,   2				)
		lowerCAmelCase					:    Union[str, Any]      							= param.transpose(1					,   2				).contiguous()
	elif checkpoint_version >= 2.0:
		# other versions store [num_heads * num_splits * hidden_size, :]
		lowerCAmelCase					:    Tuple      							= (num_heads, num_splits, hidden_size) + input_shape[1:]
		lowerCAmelCase					:    Any      							= param.view(*_A				)
		lowerCAmelCase					:    str      							= param.transpose(0					,   1				).contiguous()
	lowerCAmelCase					:    Optional[int]      							= param.view(*_A				)
	return param

def 			__UpperCamelCase    (    _A    :				Union[str, Any]					,   _A    :				Tuple					,   _A    :				Optional[int]				)    ->      str:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= {}

	# old versions did not store training args
	lowerCAmelCase					:    Any      							= input_state_dict.get('args'					,   _A				)
	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))

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

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

	# The model.
	lowerCAmelCase					:    Tuple      							= input_state_dict['model']
	# The language model.
	lowerCAmelCase					:    int      							= model['language_model']
	# The embeddings.
	lowerCAmelCase					:    Dict      							= lm['embedding']

	# The word embeddings.
	lowerCAmelCase					:    Union[str, Any]      							= embeddings['word_embeddings']['weight']
	# Truncate the embedding table to vocab_size rows.
	lowerCAmelCase					:    List[str]      							= word_embeddings[: config.vocab_size, :]
	lowerCAmelCase					:    int      							= word_embeddings

	# The position embeddings.
	lowerCAmelCase					:    Optional[Any]      							= embeddings['position_embeddings']['weight']
	# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
	lowerCAmelCase					:    int      							= 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.
	lowerCAmelCase					:    int      							= pos_embeddings

	# The transformer.
	lowerCAmelCase					:    Union[str, Any]      							= lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

	# The regex to extract layer names.
	lowerCAmelCase					:    Dict      							= re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'				)

	# The simple map of names for "automated" rules.
	lowerCAmelCase					:    str      							= {
	    '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.
		lowerCAmelCase					:    Optional[Any]      							= layer_re.match(_A				)

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

		# The index of the layer.
		lowerCAmelCase					:    int      							= int(m.group(1				)				)
		# The name of the operation.
		lowerCAmelCase					:    str      							= m.group(2				)
		# Is it a weight or a bias?
		lowerCAmelCase					:    Tuple      							= m.group(3				)

		# The name of the layer.
		lowerCAmelCase					:    Any      							= F"transformer.h.{layer_idx}"

		# For layernorm(s), simply store the layer norm.
		if op_name.endswith('layernorm'				):
			lowerCAmelCase					:    Tuple      							= 'ln_1' if op_name.startswith('input'				) else 'ln_2'
			lowerCAmelCase					:    str      							= 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.
			lowerCAmelCase					:    int      							= torch.tril(torch.ones((n_positions, n_positions)					,   dtype=torch.floataa				)				).view(
			    1					,   1					,   _A					,   _A				)
			lowerCAmelCase					:    Optional[int]      							= causal_mask

			# Insert a "dummy" tensor for masked_bias.
			lowerCAmelCase					:    Optional[int]      							= torch.tensor(-1e4					,   dtype=torch.floataa				)
			lowerCAmelCase					:    str      							= masked_bias

			lowerCAmelCase					:    int      							= fix_query_key_value_ordering(_A					,   _A					,   3					,   _A					,   _A				)
			# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
			lowerCAmelCase					:    Optional[Any]      							= out_val.transpose(0					,   1				).contiguous()
			# Store.
			lowerCAmelCase					:    List[Any]      							= 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":
			lowerCAmelCase					:    Optional[Any]      							= fix_query_key_value_ordering(_A					,   _A					,   3					,   _A					,   _A				)
			# Store. No change of shape.
			lowerCAmelCase					:    List[Any]      							= out_val

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

		# Copy the bias.
		elif weight_or_bias == "bias":
			lowerCAmelCase					:    Union[str, Any]      							= megatron_to_transformers[op_name]
			lowerCAmelCase					:    Tuple      							= val

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

	# The final layernorm.
	lowerCAmelCase					:    Tuple      							= transformer['final_layernorm.weight']
	lowerCAmelCase					:    List[Any]      							= transformer['final_layernorm.bias']

	# For LM head, transformers' wants the matrix to weight embeddings.
	lowerCAmelCase					:    List[str]      							= word_embeddings

	# It should be done!
	return output_state_dict

def 			__UpperCamelCase    (    )    ->      str:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= argparse.ArgumentParser()
	parser.add_argument('--print-checkpoint-structure'					,   action='store_true'				)
	parser.add_argument(
	    'path_to_checkpoint'					,   type=_A					,   help='Path to the checkpoint file (.zip archive or direct .pt file)'					,   )
	parser.add_argument(
	    '--config_file'					,   default=''					,   type=_A					,   help='An optional config json file describing the pre-trained model.'					,   )
	lowerCAmelCase					:    List[str]      							= parser.parse_args()

	# Extract the basename.
	lowerCAmelCase					:    Optional[Any]      							= 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:
				lowerCAmelCase					:    List[Any]      							= torch.load(_A					,   map_location='cpu'				)
	else:
		lowerCAmelCase					:    Any      							= torch.load(args.path_to_checkpoint					,   map_location='cpu'				)

	lowerCAmelCase					:    str      							= input_state_dict.get('args'					,   _A				)

	# 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:
				lowerCAmelCase					:    int      							= 'gelu_fast'
			elif ds_args.openai_gelu:
				lowerCAmelCase					:    List[str]      							= 'gelu_new'
			else:
				lowerCAmelCase					:    Any      							= 'gelu'
		else:
			# in the very early days this used to be "gelu_new"
			lowerCAmelCase					:    Union[str, Any]      							= 'gelu_new'

		# Spell out all parameters in case the defaults change.
		lowerCAmelCase					:    List[Any]      							= 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=_A					,   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=_A					,   summary_activation=_A					,   summary_proj_to_labels=_A					,   summary_first_dropout=0.1					,   scale_attn_weights=_A					,   use_cache=_A					,   bos_token_id=5_02_56					,   eos_token_id=5_02_56					,   )
	else:
		lowerCAmelCase					:    Dict      							= GPTaConfig.from_json_file(args.config_file				)

	lowerCAmelCase					:    str      							= ['GPT2LMHeadModel']

	# Convert.
	print('Converting'				)
	lowerCAmelCase					:    List[Any]      							= convert_megatron_checkpoint(_A					,   _A					,   _A				)

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

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

	lowerCAmelCase					:    List[str]      							= AutoTokenizer.from_pretrained(_A				)
	lowerCAmelCase					:    Dict      							= type(_A				).__name__
	lowerCAmelCase					:    str      							= tokenizer_class

	# Store the config to file.
	print('Saving config'				)
	config.save_pretrained(_A				)

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

	# Store the state_dict to file.
	lowerCAmelCase					:    Tuple      							= os.path.join(_A					,   'pytorch_model.bin'				)
	print(F"Saving checkpoint to \"{output_checkpoint_file}\""				)
	torch.save(_A					,   _A				)


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

if __name__ == "__main__":
				main()

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




'''simple docstring'''




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase  :		Union[str, Any]		     =	get_tests_dir('fixtures/test_sentencepiece_no_bos.model')



@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					str			      = PegasusTokenizer
	_lowerCamelCase   :					Union[str, Any]			      = PegasusTokenizerFast
	_lowerCamelCase   :					Optional[Any]			      = True
	_lowerCamelCase   :					Optional[Any]			      = True



	def 				lowercase     (					self    ):
		super().setUp()

		# We have a SentencePiece fixture for testing
		lowerCAmelCase					:    List[Any]      							= PegasusTokenizer(snake_case__    )
		tokenizer.save_pretrained(self.tmpdirname    )



	@cached_property
	def 				lowercase     (					self    ):
		return PegasusTokenizer.from_pretrained('google/pegasus-large'    )



	def 				lowercase     (					self ,					**snake_case__    ):
		return PegasusTokenizer.from_pretrained(self.tmpdirname ,					**snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		return ("This is a test", "This is a test")



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= '</s>'
		lowerCAmelCase					:    int      							= 1

		self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__    ) ,					snake_case__    )
		self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__    ) ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= list(self.get_tokenizer().get_vocab().keys()    )

		self.assertEqual(vocab_keys[0] ,					'<pad>'    )
		self.assertEqual(vocab_keys[1] ,					'</s>'    )
		self.assertEqual(vocab_keys[-1] ,					'v'    )
		self.assertEqual(len(snake_case__    ) ,					1103    )



	def 				lowercase     (					self    ):
		self.assertEqual(self.get_tokenizer().vocab_size ,					1103    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    List[Any]      							= self.tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    Optional[Any]      							= (
		    'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
		    ' </s> <pad> <pad> <pad>'
		)
		lowerCAmelCase					:    Optional[Any]      							= rust_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		lowerCAmelCase					:    Optional[int]      							= py_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self._large_tokenizer
		# <mask_1> masks whole sentence while <mask_2> masks single word
		lowerCAmelCase					:    List[str]      							= '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
		lowerCAmelCase					:    Optional[Any]      							= [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
		lowerCAmelCase					:    Optional[Any]      							= tokenizer([raw_input_str] ,					return_tensors=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self._large_tokenizer
		# The tracebacks for the following asserts are **better** without messages or self.assertEqual
		assert tokenizer.vocab_size == 9_6103
		assert tokenizer.pad_token_id == 0
		assert tokenizer.eos_token_id == 1
		assert tokenizer.offset == 103
		assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
		assert tokenizer.unk_token == "<unk>"
		assert tokenizer.model_max_length == 1024
		lowerCAmelCase					:    List[Any]      							= 'To ensure a smooth flow of bank resolutions.'
		lowerCAmelCase					:    Optional[int]      							= [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
		lowerCAmelCase					:    Any      							= tokenizer([raw_input_str] ,					return_tensors=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )
		assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]    ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



	@require_torch
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= ['This is going to be way too long.' * 150, 'short example']
		lowerCAmelCase					:    int      							= ['not super long but more than 5 tokens', 'tiny']
		lowerCAmelCase					:    Dict      							= self._large_tokenizer(snake_case__ ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )
		lowerCAmelCase					:    Dict      							= self._large_tokenizer(
		    text_target=snake_case__ ,					max_length=5 ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )

		assert batch.input_ids.shape == (2, 1024)
		assert batch.attention_mask.shape == (2, 1024)
		assert targets["input_ids"].shape == (2, 5)
		assert len(snake_case__    ) == 2  # input_ids, attention_mask.



	@slow
	def 				lowercase     (					self    ):
		# fmt: off
		lowerCAmelCase					:    Tuple      							= {'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
		# fmt: on

		self.tokenizer_integration_test_util(
		    expected_encoding=snake_case__ ,					model_name='google/bigbird-pegasus-large-arxiv' ,					revision='ba85d0851d708441f91440d509690f1ab6353415' ,					)



@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Optional[Any]			      = PegasusTokenizer
	_lowerCamelCase   :					str			      = PegasusTokenizerFast
	_lowerCamelCase   :					Tuple			      = True
	_lowerCamelCase   :					int			      = True



	def 				lowercase     (					self    ):
		super().setUp()

		# We have a SentencePiece fixture for testing
		lowerCAmelCase					:    int      							= PegasusTokenizer(snake_case__ ,					offset=0 ,					mask_token_sent=snake_case__ ,					mask_token='[MASK]'    )
		tokenizer.save_pretrained(self.tmpdirname    )



	@cached_property
	def 				lowercase     (					self    ):
		return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'    )



	def 				lowercase     (					self ,					**snake_case__    ):
		return PegasusTokenizer.from_pretrained(self.tmpdirname ,					**snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		return ("This is a test", "This is a test")



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    Union[str, Any]      							= self.tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    List[str]      							= (
		    'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
		    ' <pad> <pad> <pad>'
		)
		lowerCAmelCase					:    Dict      							= rust_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		lowerCAmelCase					:    Union[str, Any]      							= py_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )



	@require_torch
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= ['This is going to be way too long.' * 1000, 'short example']
		lowerCAmelCase					:    Union[str, Any]      							= ['not super long but more than 5 tokens', 'tiny']
		lowerCAmelCase					:    List[str]      							= self._large_tokenizer(snake_case__ ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )
		lowerCAmelCase					:    List[str]      							= self._large_tokenizer(
		    text_target=snake_case__ ,					max_length=5 ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )

		assert batch.input_ids.shape == (2, 4096)
		assert batch.attention_mask.shape == (2, 4096)
		assert targets["input_ids"].shape == (2, 5)
		assert len(snake_case__    ) == 2  # input_ids, attention_mask.



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= (
		    'This is an example string that is used to test the original TF implementation against the HF'
		    ' implementation'
		)

		lowerCAmelCase					:    Tuple      							= self._large_tokenizer(snake_case__    ).input_ids

		self.assertListEqual(
		    snake_case__ ,					[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] ,					)
