




import argparse
import json
import os
import re

import torch

from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging


logging.set_verbosity_info()

__UpperCamelCase					:       Dict    					=      [
    "word_embeddings_layernorm.weight",
    "word_embeddings_layernorm.bias",
    "input_layernorm.weight",
    "input_layernorm.bias",
    "post_attention_layernorm.weight",
    "post_attention_layernorm.bias",
    "self_attention.dense.bias",
    "mlp.dense_4h_to_h.bias",
    "ln_f.weight",
    "ln_f.bias",
]

__UpperCamelCase					:       Tuple    					=      [
    "mlp.dense_4h_to_h.weight",
    "self_attention.dense.weight",
]





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       Any:
	a										=							{
	    """word_embeddings.weight""": """word_embeddings.weight""",
	    """word_embeddings.norm.weight""": """word_embeddings_layernorm.weight""",
	    """word_embeddings.norm.bias""": """word_embeddings_layernorm.bias""",
	    """weight""": """ln_f.weight""",
	    """bias""": """ln_f.bias""",
	}

	if key in layer_rename_map:
		return layer_rename_map[key]

	# Handle transformer blocks
	a										=							int(re.match(R""".*layer_(\d*).*"""							,			__lowerCamelCase    )[1]    )
	layer_number -= 3
	return f'h.{layer_number}.' + key





def 		__A		(	__lowerCamelCase    )      ->       Any:
	if dtype == torch.bool:
		return 1 / 8
	a										=							re.search(R"""[^\d](\d+)$"""							,			str(__lowerCamelCase    )    )
	if bit_search is None:
		raise ValueError(f'`dtype` is not a valid dtype: {dtype}.'    )
	a										=							int(bit_search.groups()[0]    )
	return bit_size // 8





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Union[str, Any]:
	# Construct model
	if bloom_config_file == "":
		a										=							BloomConfig()
	else:
		a										=							BloomConfig.from_json_file(__lowerCamelCase    )

	if shard_model:
		a										=							os.listdir(__lowerCamelCase    )
		a										=							sorted(filter(lambda __lowerCamelCase    : s.startswith("""layer"""    ) and "model_00" in s							,			__lowerCamelCase    )    )

		a										=							{"""weight_map""": {}, """metadata""": {}}
		a										=							0

		a										=							None

		a										=							BloomConfig()

		for j, file in enumerate(__lowerCamelCase    ):
			print("""Processing file: {}""".format(__lowerCamelCase    )    )
			a										=							None

			for i in range(__lowerCamelCase    ):
				# load all TP files
				a										=							file.replace("""model_00"""							,			f'model_0{i}'    )
				a										=							torch.load(os.path.join(__lowerCamelCase							,			__lowerCamelCase    )							,			map_location="""cpu"""    )

				# Rename keys in the transformers names
				a										=							list(temp.keys()    )
				for key in keys:
					a										=							temp.pop(__lowerCamelCase    )

				if tensors is None:
					a										=							temp
				else:
					for key in tensors.keys():
						if any(key.endswith(__lowerCamelCase    ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH    ):
							# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
							tensors[key] += temp[key]
						else:
							# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
							a										=							1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN    ) else 0
							# We concatenate these weights accross TP ranks
							a										=							torch.cat([tensors[key], temp[key]]							,			dim=__lowerCamelCase    )

            # Divide by the number of TP the weights we want to average
			for key in tensors.keys():
				if any(key.endswith(__lowerCamelCase    ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH    ):
					a										=							tensors[key] / pretraining_tp
			torch.save(
			    __lowerCamelCase							,			os.path.join(
			        __lowerCamelCase							,			"""pytorch_model_{}-of-{}.bin""".format(str(j + 1    ).zfill(5    )							,			str(len(__lowerCamelCase    )    ).zfill(5    )    )							,			)							,			)

			for key in tensors.keys():
				a										=							tensors[key]
				total_size += value.numel() * get_dtype_size(value.dtype    )
				if key not in index_dict["weight_map"]:
					a										=							"""pytorch_model_{}-of-{}.bin""".format(
					    str(j + 1    ).zfill(5    )							,			str(len(__lowerCamelCase    )    ).zfill(5    )    )

		a										=							BloomConfig()
		a										=							pytorch_dump_folder_path + """/""" + CONFIG_NAME
		a										=							total_size
		with open(__lowerCamelCase							,			"""w"""							,			encoding="""utf-8"""    ) as f:
			f.write(config.to_json_string()    )
		with open(os.path.join(__lowerCamelCase							,			WEIGHTS_NAME + """.index.json"""    )							,			"""w"""							,			encoding="""utf-8"""    ) as f:
			a										=							json.dumps(__lowerCamelCase							,			indent=2							,			sort_keys=__lowerCamelCase    ) + """\n"""
			f.write(__lowerCamelCase    )
	else:
		a										=							BloomModel(__lowerCamelCase    )

		a										=							os.listdir(__lowerCamelCase    )
		a										=							sorted(filter(lambda __lowerCamelCase    : s.startswith("""layer"""    ) and "model_00" in s							,			__lowerCamelCase    )    )

		a										=							None
		for i, file in enumerate(__lowerCamelCase    ):
			a										=							None
			for i in range(__lowerCamelCase    ):
				# load all TP files
				a										=							file.replace("""model_00"""							,			f'model_0{i}'    )
				a										=							torch.load(os.path.join(__lowerCamelCase							,			__lowerCamelCase    )							,			map_location="""cpu"""    )

				# Rename keys in the transformers names
				a										=							list(temp.keys()    )
				for key in keys:
					a										=							temp.pop(__lowerCamelCase    )

				if tensors is None:
					a										=							temp
				else:
					for key in tensors.keys():
						# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
						if any(key.endswith(__lowerCamelCase    ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH    ):
							tensors[key] += temp[key]
						else:
							# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
							a										=							1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN    ) else 0
							# We concatenate these weights accross TP ranks
							a										=							torch.cat([tensors[key], temp[key]]							,			dim=__lowerCamelCase    )

            # Divide by the number of TP the weights we want to average
			for key in tensors.keys():
				if any(key.endswith(__lowerCamelCase    ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH    ):
					a										=							tensors[key] / pretraining_tp

			a										=							model.load_state_dict(__lowerCamelCase							,			strict=__lowerCamelCase    )
			assert not other_keys.unexpected_keys, f'The keys {other_keys.unexpected_keys} are unexpected'
			if missing_keys is None:
				a										=							set(other_keys.missing_keys    )
			else:
				a										=							missing_keys.intersection(set(other_keys.missing_keys    )    )

		assert not missing_keys, f'The keys {missing_keys} are missing'

		# Save pytorch-model
		os.makedirs(__lowerCamelCase							,			exist_ok=__lowerCamelCase    )
		a										=							pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		a										=							pytorch_dump_folder_path + """/""" + CONFIG_NAME
		print(f'Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}'    )
		if config.torch_dtype is not None:
			a										=							model.to(config.torch_dtype    )
		torch.save(model.state_dict()							,			__lowerCamelCase    )
		print(f'Save configuration file to {pytorch_config_dump_path}'    )
		with open(__lowerCamelCase							,			"""w"""							,			encoding="""utf-8"""    ) as f:
			f.write(config.to_json_string()    )


if __name__ == "__main__":
		__UpperCamelCase					:       Tuple    					=      argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--bloom_checkpoint_path",
		    default=None,
		    type=str,
		    required=True,
		    help="Path to the Megatron-LM checkpoint path.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--bloom_config_file",
		    default="",
		    type=str,
		    help=(
		        "An optional config json file corresponding to the pre-trained model. \n"
		        "This specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--shard_model",
		    action="store_true",
		    help="An optional setting to shard the output model \nThis enables sharding the converted checkpoint",
		)
		parser.add_argument(
		    "--pretraining_tp",
		    default=4,
		    type=int,
		    help="Pretraining TP rank that has been used when training the model in Megatron-LM \n",
		)
		__UpperCamelCase					:       Optional[Any]    					=      parser.parse_args()
		convert_bloom_checkpoint_to_pytorch(
		    args.bloom_checkpoint_path,
		    args.bloom_config_file,
		    args.pytorch_dump_folder_path,
		    args.shard_model,
		    args.pretraining_tp,
		)







import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  CLIPTokenizer
							UpperCamelCase__					     =  CLIPTokenizerFast
							UpperCamelCase__					     =  True
							UpperCamelCase__					     =  {}
							UpperCamelCase__					     =  False
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								super().setUp()

								# fmt: off
								a										=							["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
								# fmt: on
								a										=							dict(zip(__magic_name__		,      range(len(__magic_name__				)				)				)				)
								a										=							["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>"""]
								a										=							{"""unk_token""": """<unk>"""}

								a										=							os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""vocab_file"""]				)
								a										=							os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""merges_file"""]				)
								with open(self.vocab_file		,      """w"""		,      encoding="""utf-8"""				) as fp:
									fp.write(json.dumps(__magic_name__				) + """\n"""				)
								with open(self.merges_file		,      """w"""		,      encoding="""utf-8"""				) as fp:
									fp.write("""\n""".join(__magic_name__				)				)
							def       lowerCamelCase__							(						self						:Optional[Any]		,      **__magic_name__						:Union[str, Any]				):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map				)
								return CLIPTokenizer.from_pretrained(self.tmpdirname		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:int		,      **__magic_name__						:Optional[Any]				):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map				)
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:int		,      __magic_name__						:List[str]				):







								'''simple docstring'''







								a										=							"""lower newer"""
								a										=							"""lower newer"""
								return input_text, output_text
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							CLIPTokenizer(self.vocab_file		,      self.merges_file		,      **self.special_tokens_map				)
								a										=							"""lower newer"""
								a										=							["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
								a										=							tokenizer.tokenize(__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								a										=							tokens + [tokenizer.unk_token]
								a										=							[10, 2, 16, 9, 3, 2, 16, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__				)		,      __magic_name__				)
							@require_ftfy
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
										a										=							self.tokenizer_class.from_pretrained(__magic_name__		,      **__magic_name__				)
										a										=							self.rust_tokenizer_class.from_pretrained(__magic_name__		,      **__magic_name__				)

										a										=							"""A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."""
										a										=							tokenizer_s.tokenize(__magic_name__				)
										a										=							tokenizer_r.tokenize(__magic_name__				)

										self.assertListEqual(__magic_name__		,      __magic_name__				)

										# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
										# with Tilde) encoded in 2 different ways
										a										=							"""xa\u0303y""" + """ """ + """x\xe3y"""
										a										=							tokenizer_s.tokenize(__magic_name__				)
										a										=							tokenizer_r.tokenize(__magic_name__				)

										self.assertListEqual(__magic_name__		,      __magic_name__				)

										# Test that the tokenization is identical on unicode of space type
										a										=							[
										    """\u0009""",  # (horizontal tab, '\t')
										    """\u000B""",  # (vertical tab)
										    """\u000C""",  # (form feed)
										    """\u0020""",  # (space, ' ')
										    """\u200E""",  # (left-to-right mark):w
										    """\u200F""",  # (right-to-left mark)
										]
										for unicode_seq in spaces_unicodes:
											a										=							tokenizer_s.tokenize(__magic_name__				)
											a										=							tokenizer_r.tokenize(__magic_name__				)

											self.assertListEqual(__magic_name__		,      __magic_name__				)

										# Test that the tokenization is identical on unicode of line break type
										a										=							[
										    """\u000A""",  # (line feed, '\n')
										    """\r\n""",  # (carriage return and line feed, '\r\n')
										    """\u000D""",  # (carriage return, '\r')
										    """\r""",  # (carriage return, '\r')
										    """\u000D""",  # (carriage return, '\r')
										    """\u2028""",  # (line separator)
										    """\u2029""",  # (paragraph separator)
										    # "\u0085", # (next line)
										]

										# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
										# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
										# space (and thus into an empty list).

										for unicode_seq in line_break_unicodes:
											a										=							tokenizer_s.tokenize(__magic_name__				)
											a										=							tokenizer_r.tokenize(__magic_name__				)

											self.assertListEqual(__magic_name__		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
										a										=							"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
										a										=							F'{text_of_1_token} {text_of_1_token}'

										a										=							self.rust_tokenizer_class.from_pretrained(
										    __magic_name__		,      use_fast=__magic_name__		,      )
										a										=							tokenizer_r(__magic_name__		,      return_offsets_mapping=__magic_name__		,      add_special_tokens=__magic_name__				)
										self.assertEqual(encoding.offset_mapping[0]		,      (0, len(__magic_name__				))				)
										self.assertEqual(
										    encoding.offset_mapping[1]		,      (len(__magic_name__				) + 1, len(__magic_name__				) + 1 + len(__magic_name__				))		,      )

										a										=							F' {text}'

										a										=							self.rust_tokenizer_class.from_pretrained(
										    __magic_name__		,      use_fast=__magic_name__		,      )
										a										=							tokenizer_r(__magic_name__		,      return_offsets_mapping=__magic_name__		,      add_special_tokens=__magic_name__				)
										self.assertEqual(encoding.offset_mapping[0]		,      (1, 1 + len(__magic_name__				))				)
										self.assertEqual(
										    encoding.offset_mapping[1]		,      (1 + len(__magic_name__				) + 1, 1 + len(__magic_name__				) + 1 + len(__magic_name__				))		,      )
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								with self.assertRaises(__magic_name__				) as context:
									self.rust_tokenizer_class.from_pretrained("""robot-test/old-clip-tokenizer"""				)

								self.assertTrue(
								    context.exception.args[0].startswith(
								        """The `backend_tokenizer` provided does not match the expected format."""				)				)
							@require_ftfy
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								super().test_tokenization_python_rust_equals()





							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								pass

