




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

__a    :						str						      =       {
    """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""",
}
__a    :						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""",
}
__a    :						List[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""",
}
__a    :						Union[str, Any]						      =       {
    """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""",
}
__a    :						Any						      =       {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
__a    :						Dict						      =       {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
__a    :						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""",
}
__a    :						Optional[int]						      =       {
    """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""",
}
__a    :						List[str]						      =       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__a    :						Optional[Any]						      =       {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__a    :						Tuple						      =       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__a    :						Union[str, Any]						      =       []
__a    :						Optional[int]						      =       [
    """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""",
]
__a    :						str						      =       IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
__a    :						Union[str, Any]						      =       IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
__a    :						Union[str, Any]						      =       IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							)     ->					List[str]:
							for attribute in key.split("."							):
														lowercase__    :			List[str]     =				getattr(SCREAMING_SNAKE_CASE__							,SCREAMING_SNAKE_CASE__							)

							if weight_type is not None:
														lowercase__    :			Any     =				getattr(SCREAMING_SNAKE_CASE__							,SCREAMING_SNAKE_CASE__							).shape
							else:
														lowercase__    :			Any     =				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":
														lowercase__    :			Tuple     =				value
							elif weight_type == "weight_g":
														lowercase__    :			str     =				value
							elif weight_type == "weight_v":
														lowercase__    :			Tuple     =				value
							elif weight_type == "bias":
														lowercase__    :			str     =				value
							elif weight_type == "running_mean":
														lowercase__    :			str     =				value
							elif weight_type == "running_var":
														lowercase__    :			Any     =				value
							elif weight_type == "num_batches_tracked":
														lowercase__    :			int     =				value
							else:
														lowercase__    :			Union[str, Any]     =				value

							logger.info(F"""{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}."""							)
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							)     ->					List[Any]:
							for key in ignore_keys:
														if key.endswith(".*"							):
																					if name.startswith(key[:-1]							):
																												return True
														elif ".*." in key:
																					lowercase__   ,  lowercase__    :			Union[str, Any]     =				key.split(".*."							)
																					if prefix in name and suffix in name:
																												return True
														elif key in name:
																					return True
							return False
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							)     ->					Tuple:
							lowercase__    :			int     =				[]

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

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

														lowercase__    :			int     =				False
														if "conv_layers" in name:
																					load_conv_layer(
																					    SCREAMING_SNAKE_CASE__							,SCREAMING_SNAKE_CASE__							,SCREAMING_SNAKE_CASE__							,SCREAMING_SNAKE_CASE__							,hf_model.config.feat_extract_norm == "group"							,)
																					lowercase__    :			Optional[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:
																																			lowercase__   ,  lowercase__    :			Any     =				key.split(".*."							)
																																			if prefix in name and suffix in name:
																																										lowercase__    :			int     =				suffix

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

							logger.warning(F"""Unused weights: {unused_weights}"""							)
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							)     ->					int:
							lowercase__    :			int     =				full_name.split("conv_layers."							)[-1]
							lowercase__    :			int     =				name.split("."							)
							lowercase__    :			List[str]     =				int(items[0]							)
							lowercase__    :			List[str]     =				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."""							)
																					lowercase__    :			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."""							)
																					lowercase__    :			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."""							)
																					lowercase__    :			List[str]     =				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."""							)
																					lowercase__    :			List[Any]     =				value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
							else:
														unused_weights.append(SCREAMING_SNAKE_CASE__							)
@torch.no_grad()
def    snake_case_	(						SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_=None							,SCREAMING_SNAKE_CASE_=None							,SCREAMING_SNAKE_CASE_=None							,)     ->					List[Any]:
							if config_path is not None:
														lowercase__    :			List[Any]     =				SpeechTaConfig.from_pretrained(SCREAMING_SNAKE_CASE__							)
							else:
														lowercase__    :			Optional[Any]     =				SpeechTaConfig()

							if task == "s2t":
														lowercase__    :			List[str]     =				config.max_text_positions
														lowercase__    :			Union[str, Any]     =				SpeechTaForSpeechToText(SCREAMING_SNAKE_CASE__							)
							elif task == "t2s":
														lowercase__    :			Optional[Any]     =				18_76
														lowercase__    :			Optional[int]     =				6_00
														lowercase__    :			Dict     =				config.max_speech_positions
														lowercase__    :			List[str]     =				SpeechTaForTextToSpeech(SCREAMING_SNAKE_CASE__							)
							elif task == "s2s":
														lowercase__    :			str     =				18_76
														lowercase__    :			Optional[Any]     =				config.max_speech_positions
														lowercase__    :			Dict     =				SpeechTaForSpeechToSpeech(SCREAMING_SNAKE_CASE__							)
							else:
														raise ValueError(F"""Unknown task name: {task}"""							)

							if vocab_path:
														lowercase__    :			Dict     =				SpeechTaTokenizer(SCREAMING_SNAKE_CASE__							,model_max_length=config.max_text_positions							)

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

							lowercase__    :			str     =				SpeechTaFeatureExtractor()
							lowercase__    :			List[str]     =				SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE__							,feature_extractor=SCREAMING_SNAKE_CASE__							)
							processor.save_pretrained(SCREAMING_SNAKE_CASE__							)

							lowercase__    :			Optional[Any]     =				torch.load(SCREAMING_SNAKE_CASE__							)
							recursively_load_weights(fairseq_checkpoint["model"]							,SCREAMING_SNAKE_CASE__							,SCREAMING_SNAKE_CASE__							)

							model.save_pretrained(SCREAMING_SNAKE_CASE__							)

							if repo_id:
														print("Pushing to the hub..."							)
														processor.push_to_hub(SCREAMING_SNAKE_CASE__							)
														model.push_to_hub(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
						__a    :						List[str]						      =       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.'''
						)

						__a    :						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 argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase   :							Tuple         =   logging.get_logger(__name__)



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[Any]     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     MobileNetVaConfig(layer_norm_eps=0.0_0_1     )

   if "_quant" in model_name:
      raise ValueError('Quantized models are not supported.'     )

   __a					  =     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$',       SCREAMING_SNAKE_CASE__     )
   if matches:
      __a					  =     float(matches[1]     )
      __a					  =     int(matches[2]     )

   # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
   # the usual 1000. The first class (index 0) is "background".
   __a					  =     1001
   __a					  =     'imagenet-1k-id2label.json'
   __a					  =     'huggingface/label-files'
   __a					  =     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       repo_type='dataset'     ),       'r'     )     )
   __a					  =     {int(SCREAMING_SNAKE_CASE__     ) + 1: v for k, v in idalabel.items()}
   __a					  =     'background'
   __a					  =     idalabel
   __a					  =     {v: k for k, v in idalabel.items()}

   return config



def  __UpperCAmelCase						(					)	->      Union[str, Any]:

   """simple docstring"""
   __a					  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   __a					  =     Image.open(requests.get(SCREAMING_SNAKE_CASE__,       stream=SCREAMING_SNAKE_CASE__     ).raw     )
   return im



@torch.no_grad()
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Union[str, Any],       SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						str=False     )	->      int:

   """simple docstring"""
   __a					  =     get_mobilenet_va_config(SCREAMING_SNAKE_CASE__     )

   # Load 🤗 model
   __a					  =     MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE__     ).eval()

   # Load weights from TensorFlow checkpoint
   load_tf_weights_in_mobilenet_va(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )

   # Check outputs on an image, prepared by MobileNetV1ImageProcessor
   __a					  =     MobileNetVaImageProcessor(
       crop_size={'width': config.image_size, 'height': config.image_size},       size={'shortest_edge': config.image_size + 32},       )
   __a					  =     image_processor(images=prepare_img(),       return_tensors='pt'     )
   __a					  =     model(**SCREAMING_SNAKE_CASE__     )
   __a					  =     outputs.logits

   assert logits.shape == (1, 1001)

   if model_name == "mobilenet_v1_1.0_224":
      __a					  =     torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]     )
   elif model_name == "mobilenet_v1_0.75_192":
      __a					  =     torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]     )
   else:
      __a					  =     None

   if expected_logits is not None:
      assert torch.allclose(logits[0, :3],       SCREAMING_SNAKE_CASE__,       atol=1e-4     )

   Path(SCREAMING_SNAKE_CASE__     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__     )
   print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""     )
   model.save_pretrained(SCREAMING_SNAKE_CASE__     )
   print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
   image_processor.save_pretrained(SCREAMING_SNAKE_CASE__     )

   if push_to_hub:
      print('Pushing to the hub...'     )
      __a					  =     'google/' + model_name
      image_processor.push_to_hub(SCREAMING_SNAKE_CASE__     )
      model.push_to_hub(SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""mobilenet_v1_1.0_224""",
     type=str,
     help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
 )
 parser.add_argument(
     """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
 )

 __UpperCamelCase   :							Any         =   parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )