


"""simple docstring"""
import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :    Dict									=				logging.get_logger("""transformers.models.speecht5""")

SCREAMING_SNAKE_CASE :    Any									=				{
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
SCREAMING_SNAKE_CASE :    List[str]									=				{
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
SCREAMING_SNAKE_CASE :    Any									=				{
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
SCREAMING_SNAKE_CASE :    int									=				{
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
SCREAMING_SNAKE_CASE :    List[Any]									=				{
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
SCREAMING_SNAKE_CASE :    Union[str, Any]									=				{
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
SCREAMING_SNAKE_CASE :    List[str]									=				{
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
SCREAMING_SNAKE_CASE :    Optional[Any]									=				{
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
SCREAMING_SNAKE_CASE :    Optional[Any]									=				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE :    Tuple									=				{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE :    List[str]									=				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE :    Optional[int]									=				[]
SCREAMING_SNAKE_CASE :    List[Any]									=				[
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
SCREAMING_SNAKE_CASE :    Tuple									=				IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
SCREAMING_SNAKE_CASE :    Dict									=				IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
SCREAMING_SNAKE_CASE :    str									=				IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]






def 		lowercase							(     _snake_case						:      Optional[Any]     ,   _snake_case						:      List[Any]     ,   _snake_case						:      List[str]     ,   _snake_case						:      Optional[int]     ,   _snake_case						:      Optional[Any]     )     ->List[Any]:
	"""simple docstring"""





	for attribute in key.split('''.'''     ):
		__snake_case	:						Union[str, Any]						=				getattr(_snake_case     ,   _snake_case     )

	if weight_type is not None:
		__snake_case	:						str						=				getattr(_snake_case     ,   _snake_case     ).shape
	else:
		__snake_case	:						str						=				hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
		    f""" {value.shape} for {full_name}"""     )

	if weight_type == "weight":
		__snake_case	:						List[Any]						=				value
	elif weight_type == "weight_g":
		__snake_case	:						Dict						=				value
	elif weight_type == "weight_v":
		__snake_case	:						List[Any]						=				value
	elif weight_type == "bias":
		__snake_case	:						Dict						=				value
	elif weight_type == "running_mean":
		__snake_case	:						Tuple						=				value
	elif weight_type == "running_var":
		__snake_case	:						Dict						=				value
	elif weight_type == "num_batches_tracked":
		__snake_case	:						Any						=				value
	else:
		__snake_case	:						Dict						=				value

	logger.info(f"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."""     )






def 		lowercase							(     _snake_case						:      List[str]     ,   _snake_case						:      int     )     ->List[str]:
	"""simple docstring"""





	for key in ignore_keys:
		if key.endswith('''.*'''     ):
			if name.startswith(key[:-1]     ):
				return True
		elif ".*." in key:
			__snake_case	,							__snake_case	:						Union[str, Any]						=				key.split('''.*.'''     )
			if prefix in name and suffix in name:
				return True
		elif key in name:
			return True
	return False






def 		lowercase							(     _snake_case						:      Optional[int]     ,   _snake_case						:      int     ,   _snake_case						:      Optional[int]     )     ->Tuple:
	"""simple docstring"""





	__snake_case	:						Tuple						=				[]

	if task == "s2t":
		__snake_case	:						List[Any]						=				hf_model.speechta.encoder.prenet.feature_encoder
		__snake_case	:						Optional[Any]						=				MAPPING_S2T
		__snake_case	:						int						=				IGNORE_KEYS_S2T
	elif task == "t2s":
		__snake_case	:						Union[str, Any]						=				None
		__snake_case	:						Tuple						=				MAPPING_T2S
		__snake_case	:						Union[str, Any]						=				IGNORE_KEYS_T2S
	elif task == "s2s":
		__snake_case	:						Union[str, Any]						=				hf_model.speechta.encoder.prenet.feature_encoder
		__snake_case	:						Any						=				MAPPING_S2S
		__snake_case	:						List[str]						=				IGNORE_KEYS_S2S
	else:
		raise ValueError(f"""Unsupported task: {task}"""     )

	for name, value in fairseq_dict.items():
		if should_ignore(_snake_case     ,   _snake_case     ):
			logger.info(f"""{name} was ignored"""     )
			continue

		__snake_case	:						Any						=				False
		if "conv_layers" in name:
			load_conv_layer(
			    _snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     ,   hf_model.config.feat_extract_norm == '''group'''     ,   )
			__snake_case	:						Dict						=				True
		else:
			for key, mapped_key in MAPPING.items():
				# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

				if "*" in key:
					__snake_case	,							__snake_case	:						List[str]						=				key.split('''.*.'''     )
					if prefix in name and suffix in name:
						__snake_case	:						int						=				suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
				if key in name:
					__snake_case	:						Optional[int]						=				True
					if "*" in mapped_key:
						__snake_case	:						Union[str, Any]						=				name.split(_snake_case     )[0].split('''.'''     )[-2]
						__snake_case	:						Optional[Any]						=				mapped_key.replace('''*'''     ,   _snake_case     )
					if "weight_g" in name:
						__snake_case	:						str						=				'''weight_g'''
					elif "weight_v" in name:
						__snake_case	:						List[Any]						=				'''weight_v'''
					elif "bias" in name:
						__snake_case	:						List[str]						=				'''bias'''
					elif "weight" in name:
						__snake_case	:						Optional[Any]						=				'''weight'''
					elif "running_mean" in name:
						__snake_case	:						Optional[Any]						=				'''running_mean'''
					elif "running_var" in name:
						__snake_case	:						Optional[int]						=				'''running_var'''
					elif "num_batches_tracked" in name:
						__snake_case	:						Union[str, Any]						=				'''num_batches_tracked'''
					else:
						__snake_case	:						str						=				None
					set_recursively(_snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     )
				continue
		if not is_used:
			unused_weights.append(_snake_case     )

	logger.warning(f"""Unused weights: {unused_weights}"""     )






def 		lowercase							(     _snake_case						:      Any     ,   _snake_case						:      str     ,   _snake_case						:      Optional[Any]     ,   _snake_case						:      Any     ,   _snake_case						:      int     )     ->List[str]:
	"""simple docstring"""





	__snake_case	:						Dict						=				full_name.split('''conv_layers.'''     )[-1]
	__snake_case	:						str						=				name.split('''.'''     )
	__snake_case	:						Union[str, Any]						=				int(items[0]     )
	__snake_case	:						Optional[Any]						=				int(items[1]     )

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    f"""{full_name} has size {value.shape}, but"""
				    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""     )
			__snake_case	:						Optional[int]						=				value
			logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    f"""{full_name} has size {value.shape}, but"""
				    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""     )
			__snake_case	:						str						=				value
			logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    f"""{full_name} has size {value.shape}, but"""
				    f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""     )
			__snake_case	:						Union[str, Any]						=				value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    f"""{full_name} has size {value.shape}, but"""
				    f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""     )
			__snake_case	:						Any						=				value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
	else:
		unused_weights.append(_snake_case     )






@torch.no_grad()
def 		lowercase							(     _snake_case						:      Tuple     ,   _snake_case						:      str     ,   _snake_case						:      int     ,   _snake_case						:      Optional[int]=None     ,   _snake_case						:      Optional[Any]=None     ,   _snake_case						:      Optional[Any]=None     ,   )     ->int:
	"""simple docstring"""





	if config_path is not None:
		__snake_case	:						str						=				SpeechTaConfig.from_pretrained(_snake_case     )
	else:
		__snake_case	:						Union[str, Any]						=				SpeechTaConfig()

	if task == "s2t":
		__snake_case	:						int						=				config.max_text_positions
		__snake_case	:						List[Any]						=				SpeechTaForSpeechToText(_snake_case     )
	elif task == "t2s":
		__snake_case	:						int						=				1_876
		__snake_case	:						List[str]						=				600
		__snake_case	:						Union[str, Any]						=				config.max_speech_positions
		__snake_case	:						Union[str, Any]						=				SpeechTaForTextToSpeech(_snake_case     )
	elif task == "s2s":
		__snake_case	:						Union[str, Any]						=				1_876
		__snake_case	:						Tuple						=				config.max_speech_positions
		__snake_case	:						List[str]						=				SpeechTaForSpeechToSpeech(_snake_case     )
	else:
		raise ValueError(f"""Unknown task name: {task}"""     )

	if vocab_path:
		__snake_case	:						str						=				SpeechTaTokenizer(_snake_case     ,   model_max_length=config.max_text_positions     )

		# Mask token behaves like a normal word, i.e. include the space before it
		__snake_case	:						Optional[Any]						=				AddedToken('''<mask>'''     ,   lstrip=_snake_case     ,   rstrip=_snake_case     )
		__snake_case	:						str						=				mask_token
		tokenizer.add_special_tokens({'''mask_token''': mask_token}     )
		tokenizer.add_tokens(['''<ctc_blank>''']     )

	__snake_case	:						str						=				SpeechTaFeatureExtractor()
	__snake_case	:						int						=				SpeechTaProcessor(tokenizer=_snake_case     ,   feature_extractor=_snake_case     )
	processor.save_pretrained(_snake_case     )

	__snake_case	:						Optional[int]						=				torch.load(_snake_case     )
	recursively_load_weights(fairseq_checkpoint['''model''']     ,   _snake_case     ,   _snake_case     )

	model.save_pretrained(_snake_case     )

	if repo_id:
		print('''Pushing to the hub...'''     )
		processor.push_to_hub(_snake_case     )
		model.push_to_hub(_snake_case     )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Tuple									=				argparse.ArgumentParser()
			parser.add_argument(
			    """--task""",
			    default="""s2t""",
			    type=str,
			    help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
			)
			parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
			parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
			parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
			parser.add_argument(
			    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
			)
			parser.add_argument(
			    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
			)

			SCREAMING_SNAKE_CASE :    List[Any]									=				parser.parse_args()
			convert_speechta_checkpoint(
			    args.task,
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.config_path,
			    args.vocab_path,
			    args.push_to_hub,
			)









"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


SCREAMING_SNAKE_CASE :    Union[str, Any]									=				"""\

"""

SCREAMING_SNAKE_CASE :    Any									=				"""
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
"""

SCREAMING_SNAKE_CASE :    Dict									=				"""
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to 'cuda' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        78.22
        >>> print(round(results[\"perplexities\"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = datasets.load_dataset(\"wikitext\",
        ...                                     \"wikitext-2-raw-v1\",
        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!='']
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        60.35
        >>> print(round(results[\"perplexities\"][0], 2))
        81.12
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,				_KWARGS_DESCRIPTION				)
class 			_UpperCAmelCase (							datasets.Metric				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return datasets.MetricInfo(
			    description=_DESCRIPTION				,       citation=_CITATION				,       inputs_description=_KWARGS_DESCRIPTION				,       features=datasets.Features(
			        {
			            '''input_texts''': datasets.Value('''string'''					),
			        }					)				,       reference_urls=['''https://huggingface.co/docs/transformers/perplexity''']				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_ = 16				,       a_ = True				,       a_=None					):





			'''simple docstring'''

			if device is not None:
				assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
				if device == "gpu":
					__snake_case	:						Optional[Any]						=				'''cuda'''
			else:
				__snake_case	:						Tuple						=				'''cuda''' if torch.cuda.is_available() else '''cpu'''

			__snake_case	:						int						=				AutoModelForCausalLM.from_pretrained(a_					)
			__snake_case	:						Optional[int]						=				model.to(a_					)

			__snake_case	:						Optional[int]						=				AutoTokenizer.from_pretrained(a_					)

			# if batch_size > 1 (which generally leads to padding being required), and
			# if there is not an already assigned pad_token, assign an existing
			# special token to also be the padding token
			if tokenizer.pad_token is None and batch_size > 1:
				__snake_case	:						List[Any]						=				list(tokenizer.special_tokens_map_extended.values()					)
				# check that the model already has at least one special token defined
				assert (
				    len(a_					) > 0
				), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
				# assign one of the special tokens to also be the pad token
				tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]}					)

			if add_start_token:
				# leave room for <BOS> token to be added:
				assert (
				    tokenizer.bos_token is not None
				), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
				__snake_case	:						List[Any]						=				model.config.max_length - 1
			else:
				__snake_case	:						Dict						=				model.config.max_length

			__snake_case	:						Tuple						=				tokenizer(
			    a_				,       add_special_tokens=a_				,       padding=a_				,       truncation=a_				,       max_length=a_				,       return_tensors='''pt'''				,       return_attention_mask=a_				,       ).to(a_					)

			__snake_case	:						List[Any]						=				encodings['''input_ids''']
			__snake_case	:						str						=				encodings['''attention_mask''']

			# check that each input is long enough:
			if add_start_token:
				assert torch.all(torch.ge(attn_masks.sum(1					)				,       1					)					), "Each input text must be at least one token long."
			else:
				assert torch.all(
				    torch.ge(attn_masks.sum(1					)				,       2					)					), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

			__snake_case	:						Union[str, Any]						=				[]
			__snake_case	:						str						=				CrossEntropyLoss(reduction='''none'''					)

			for start_index in logging.tqdm(range(0				,       len(a_					)				,       a_					)					):
				__snake_case	:						Optional[int]						=				min(start_index + batch_size				,       len(a_					)					)
				__snake_case	:						int						=				encoded_texts[start_index:end_index]
				__snake_case	:						Optional[int]						=				attn_masks[start_index:end_index]

				if add_start_token:
					__snake_case	:						List[Any]						=				torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0					)					).to(a_					)
					__snake_case	:						Union[str, Any]						=				torch.cat([bos_tokens_tensor, encoded_batch]				,       dim=1					)
					__snake_case	:						int						=				torch.cat(
					    [torch.ones(bos_tokens_tensor.size()				,       dtype=torch.intaa					).to(a_					), attn_mask]				,       dim=1					)

				__snake_case	:						List[Any]						=				encoded_batch

				with torch.no_grad():
					__snake_case	:						List[str]						=				model(a_				,       attention_mask=a_					).logits

				__snake_case	:						List[str]						=				out_logits[..., :-1, :].contiguous()
				__snake_case	:						int						=				labels[..., 1:].contiguous()
				__snake_case	:						int						=				attn_mask[..., 1:].contiguous()

				__snake_case	:						Tuple						=				torch.expa(
				    (loss_fct(shift_logits.transpose(1				,       2					)				,       a_					) * shift_attention_mask_batch).sum(1					)
				    / shift_attention_mask_batch.sum(1					)					)

				ppls += perplexity_batch.tolist()

			return {"perplexities": ppls, "mean_perplexity": np.mean(a_					)}





