



"""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()
_lowercase			:			Union[str, Any]										=						logging.get_logger("transformers.models.speecht5")

_lowercase			:			Optional[int]										=						{
    "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",
}
_lowercase			:			Union[str, Any]										=						{
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
_lowercase			:			int										=						{
    "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",
}
_lowercase			:			Dict										=						{
    "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",
}
_lowercase			:			Optional[int]										=						{
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
_lowercase			:			Dict										=						{
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
_lowercase			:			Any										=						{
    "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",
}
_lowercase			:			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",
}
_lowercase			:			List[Any]										=						{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_lowercase			:			Optional[Any]										=						{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowercase			:			List[str]										=						{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowercase			:			Optional[Any]										=						[]
_lowercase			:			str										=						[
    "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",
]
_lowercase			:			Dict										=						IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
_lowercase			:			Optional[Any]										=						IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
_lowercase			:			Tuple										=						IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   List[Any]							,		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   List[str]     ):
			"""simple docstring"""
			for attribute in key.split('''.'''     ):
						lowerCamelCase__		:		Tuple		     =getattr(__lowerCamelCase							,		__lowerCamelCase     )

			if weight_type is not None:
						lowerCamelCase__		:		Optional[int]		     =getattr(__lowerCamelCase							,		__lowerCamelCase     ).shape
			else:
						lowerCamelCase__		:		Dict		     =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":
						lowerCamelCase__		:		str		     =value
			elif weight_type == "weight_g":
						lowerCamelCase__		:		Optional[Any]		     =value
			elif weight_type == "weight_v":
						lowerCamelCase__		:		int		     =value
			elif weight_type == "bias":
						lowerCamelCase__		:		int		     =value
			elif weight_type == "running_mean":
						lowerCamelCase__		:		List[str]		     =value
			elif weight_type == "running_var":
						lowerCamelCase__		:		List[Any]		     =value
			elif weight_type == "num_batches_tracked":
						lowerCamelCase__		:		str		     =value
			else:
						lowerCamelCase__		:		Tuple		     =value

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






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   List[str]     ):
			"""simple docstring"""
			for key in ignore_keys:
						if key.endswith('''.*'''     ):
									if name.startswith(key[:-1]     ):
												return True
						elif ".*." in key:
									lowerCamelCase__		:		int		     =key.split('''.*.'''     )
									if prefix in name and suffix in name:
												return True
						elif key in name:
									return True
			return False






def 					snake_case__       (		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Tuple     ):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     =[]

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

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

						lowerCamelCase__		:		str		     =False
						if "conv_layers" in name:
									load_conv_layer(
									    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		hf_model.config.feat_extract_norm == '''group'''							,		)
									lowerCamelCase__		:		Any		     =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:
															lowerCamelCase__		:		Tuple		     =key.split('''.*.'''     )
															if prefix in name and suffix in name:
																		lowerCamelCase__		:		Union[str, Any]		     =suffix

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

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






def 					snake_case__       (		__lowerCamelCase    :   Optional[int]							,		__lowerCamelCase    :   Optional[Any]							,		__lowerCamelCase    :   List[Any]							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   Dict     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =full_name.split('''conv_layers.'''     )[-1]
			lowerCamelCase__		:		Union[str, Any]		     =name.split('''.'''     )
			lowerCamelCase__		:		Dict		     =int(items[0]     )
			lowerCamelCase__		:		Dict		     =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.'''     )
									lowerCamelCase__		:		Union[str, Any]		     =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.'''     )
									lowerCamelCase__		:		List[Any]		     =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.'''     )
									lowerCamelCase__		:		Dict		     =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.'''     )
									lowerCamelCase__		:		Any		     =value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
			else:
						unused_weights.append(__lowerCamelCase     )






@torch.no_grad()
def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   List[Any]							,		__lowerCamelCase    :   Any=None							,		__lowerCamelCase    :   Union[str, Any]=None							,		__lowerCamelCase    :   Optional[Any]=None							,		):
			"""simple docstring"""
			if config_path is not None:
						lowerCamelCase__		:		Tuple		     =SpeechTaConfig.from_pretrained(__lowerCamelCase     )
			else:
						lowerCamelCase__		:		str		     =SpeechTaConfig()

			if task == "s2t":
						lowerCamelCase__		:		Optional[int]		     =config.max_text_positions
						lowerCamelCase__		:		List[str]		     =SpeechTaForSpeechToText(__lowerCamelCase     )
			elif task == "t2s":
						lowerCamelCase__		:		List[str]		     =1876
						lowerCamelCase__		:		List[str]		     =600
						lowerCamelCase__		:		int		     =config.max_speech_positions
						lowerCamelCase__		:		Dict		     =SpeechTaForTextToSpeech(__lowerCamelCase     )
			elif task == "s2s":
						lowerCamelCase__		:		Optional[int]		     =1876
						lowerCamelCase__		:		Dict		     =config.max_speech_positions
						lowerCamelCase__		:		str		     =SpeechTaForSpeechToSpeech(__lowerCamelCase     )
			else:
						raise ValueError(f'''Unknown task name: {task}'''     )

			if vocab_path:
						lowerCamelCase__		:		List[str]		     =SpeechTaTokenizer(__lowerCamelCase							,		model_max_length=config.max_text_positions     )

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

			lowerCamelCase__		:		Tuple		     =SpeechTaFeatureExtractor()
			lowerCamelCase__		:		Tuple		     =SpeechTaProcessor(tokenizer=__lowerCamelCase							,		feature_extractor=__lowerCamelCase     )
			processor.save_pretrained(__lowerCamelCase     )

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

			model.save_pretrained(__lowerCamelCase     )

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


if __name__ == "__main__":
		_lowercase			:			Dict										=						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."
		)

		_lowercase			:			Optional[int]										=						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"""






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_lowercase			:			List[str]										=						logging.get_logger(__name__)






def 					snake_case__       (		__lowerCamelCase    :   Union[tf.Tensor, np.ndarray]     ):
			"""simple docstring"""
			if isinstance(__lowerCamelCase							,		np.ndarray     ):
						return list(tensor.shape     )

			lowerCamelCase__		:		List[Any]		     =tf.shape(__lowerCamelCase     )

			if tensor.shape == tf.TensorShape(__lowerCamelCase     ):
						return dynamic

			lowerCamelCase__		:		List[str]		     =tensor.shape.as_list()

			return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCamelCase     )]






def 					snake_case__       (		__lowerCamelCase    :   tf.Tensor							,		__lowerCamelCase    :   Optional[int] = None							,		__lowerCamelCase    :   Optional[str] = None     ):
			"""simple docstring"""
			return tf.nn.softmax(logits=logits + 1e-9							,		axis=__lowerCamelCase							,		name=__lowerCamelCase     )






def 					snake_case__       (		__lowerCamelCase    :   Optional[Any]							,		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Optional[int]							,		__lowerCamelCase    :   Any=1e-5							,		__lowerCamelCase    :   str=-1     ):
			"""simple docstring"""
			# This is a very simplified functional layernorm, designed to duplicate
			# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
			# models in Transformers.

			if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCamelCase							,		__lowerCamelCase     ):
						raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''     )

			# Get mean and variance on the axis to be normalized
			lowerCamelCase__     ,       lowerCamelCase__		:		Optional[int]		     =tf.nn.moments(__lowerCamelCase							,		axes=[axis]							,		keepdims=__lowerCamelCase     )

			if axis != -1:
						# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
						# on every dimension except axis
						lowerCamelCase__		:		Optional[Any]		     =[1] * inputs.shape.rank
						lowerCamelCase__		:		Union[str, Any]		     =shape_list(__lowerCamelCase     )[axis]
						lowerCamelCase__		:		Optional[Any]		     =tf.reshape(__lowerCamelCase							,		__lowerCamelCase     )
						lowerCamelCase__		:		Any		     =tf.reshape(__lowerCamelCase							,		__lowerCamelCase     )

			# Compute layer normalization using the batch_normalization
			# function.
			lowerCamelCase__		:		List[str]		     =tf.nn.batch_normalization(
			    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		offset=__lowerCamelCase							,		scale=__lowerCamelCase							,		variance_epsilon=__lowerCamelCase							,		)
			return outputs






def 					snake_case__       (		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   List[Any]=0							,		__lowerCamelCase    :   int=-1     ):
			"""simple docstring"""
			# Replicates the behavior of torch.flatten in TF

			# If end_dim or start_dim is negative, count them from the end
			if end_dim < 0:
						end_dim += input.shape.rank
			if start_dim < 0:
						start_dim += input.shape.rank

			if start_dim == end_dim:
						return input

			lowerCamelCase__		:		Optional[int]		     =tf.shape(__lowerCamelCase     )
			lowerCamelCase__		:		Optional[int]		     =tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]     )
			lowerCamelCase__		:		List[Any]		     =tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]							,		axis=0     )
			return tf.reshape(__lowerCamelCase							,		__lowerCamelCase     )






def 					snake_case__       (		__lowerCamelCase    :   tf.Tensor     ):
			"""simple docstring"""
			if not isinstance(__lowerCamelCase							,		tf.Tensor     ):
						lowerCamelCase__		:		List[Any]		     =tf.convert_to_tensor(__lowerCamelCase     )  # Catches stray NumPy inputs
			if encoder_attention_mask.shape.rank == 3:
						lowerCamelCase__		:		int		     =encoder_attention_mask[:, None, :, :]
			if encoder_attention_mask.shape.rank == 2:
						lowerCamelCase__		:		int		     =encoder_attention_mask[:, None, None, :]
			# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
			# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
			# /transformer/transformer_layers.py#L270
			# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
			# encoder_extended_attention_mask.transpose(-1, -2))
			lowerCamelCase__		:		str		     =(
			    tf.cast(1							,		encoder_attention_mask.dtype     ) - encoder_extended_attention_mask
			) * encoder_extended_attention_mask.dtype.min

			return encoder_extended_attention_mask






def 					snake_case__       (		__lowerCamelCase    :   tf.Tensor							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   str = "input_ids"     ):
			"""simple docstring"""
			tf.debugging.assert_less(
			    __lowerCamelCase							,		tf.cast(__lowerCamelCase							,		dtype=tensor.dtype     )							,		message=(
			        f'''The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCamelCase     )}) must be smaller than the embedding '''
			        f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
			    )							,		)






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Dict     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =64512
			# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
			# because in that case even chunking the array would not make the saving
			# possible.
			lowerCamelCase__		:		Tuple		     =[x for x in data if len(__lowerCamelCase     ) > HDF5_OBJECT_HEADER_LIMIT]

			# Expecting this to never be true.
			if bad_attributes:
						raise RuntimeError(
						    '''The following attributes cannot be saved to HDF5 file because '''
						    f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
						    f'''bytes: {bad_attributes}'''     )

			lowerCamelCase__		:		Optional[Any]		     =np.asarray(__lowerCamelCase     )

			lowerCamelCase__		:		str		     =1
			lowerCamelCase__		:		List[Any]		     =np.array_split(__lowerCamelCase							,		__lowerCamelCase     )

			# This will never loop forever thanks to the test above.
			while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data     ):
						num_chunks += 1
						lowerCamelCase__		:		Union[str, Any]		     =np.array_split(__lowerCamelCase							,		__lowerCamelCase     )

			if num_chunks > 1:
						for chunk_id, chunk_data in enumerate(__lowerCamelCase     ):
									lowerCamelCase__		:		List[str]		     =chunk_data
			else:
						lowerCamelCase__		:		Dict		     =data






def 					snake_case__       (		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   Optional[Any]     ):
			"""simple docstring"""
			if name in group.attrs:
						lowerCamelCase__		:		Optional[int]		     =[n.decode('''utf8'''     ) if hasattr(__lowerCamelCase							,		'''decode'''     ) else n for n in group.attrs[name]]
			else:
						lowerCamelCase__		:		str		     =[]
						lowerCamelCase__		:		str		     =0
						while "%s%d" % (name, chunk_id) in group.attrs:
									data.extend(
									    [n.decode('''utf8'''     ) if hasattr(__lowerCamelCase							,		'''decode'''     ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]     )
									chunk_id += 1
			return data






def 					snake_case__       (		__lowerCamelCase    :   Dict     ):
			"""simple docstring"""

			def _expand_single_ad_tensor(__lowerCamelCase    :   List[Any]     ):
						if isinstance(__lowerCamelCase							,		tf.Tensor     ) and t.shape.rank == 1:
									return tf.expand_dims(__lowerCamelCase							,		axis=-1     )
						return t

			return tf.nest.map_structure(_expand_single_ad_tensor							,		__lowerCamelCase     )




