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__		:					Optional[Any]						=					logging.get_logger("transformers.models.speecht5")

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







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			Optional[int]     ,  lowercase__  :			Tuple     ,  lowercase__  :			List[Any]     ,  lowercase__  :			Union[str, Any] )     ->   str:







     '''simple docstring'''


     for attribute in key.split('.' ):
          SCREAMING_SNAKE_CASE__				:				Tuple			=		getattr(lowercase__     ,  lowercase__ )

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

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







def     _a				(      lowercase__  :			List[Any]     ,  lowercase__  :			Tuple )     ->   Union[str, Any]:







     '''simple docstring'''


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







def     _a				(      lowercase__  :			Optional[int]     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			Dict )     ->   List[Any]:







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[str]			=		[]

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

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

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

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

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







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			Optional[int]     ,  lowercase__  :			Tuple     ,  lowercase__  :			Any     ,  lowercase__  :			Any )     ->   Optional[int]:







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		full_name.split('conv_layers.' )[-1]
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		name.split('.' )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		int(items[0] )
     SCREAMING_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.''' )
               SCREAMING_SNAKE_CASE__				:				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.''' )
               SCREAMING_SNAKE_CASE__				:				Optional[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.''' )
               SCREAMING_SNAKE_CASE__				:				int			=		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.''' )
               SCREAMING_SNAKE_CASE__				:				Dict			=		value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
     else:
          unused_weights.append(lowercase__ )







@torch.no_grad()
def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			Optional[int]     ,  lowercase__  :			Optional[int]     ,  lowercase__  :			List[Any]=None     ,  lowercase__  :			Optional[Any]=None     ,  lowercase__  :			Any=None     ,  )     ->   str:







     '''simple docstring'''


     if config_path is not None:
          SCREAMING_SNAKE_CASE__				:				int			=		SpeechTaConfig.from_pretrained(lowercase__ )
     else:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		SpeechTaConfig()

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

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

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

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

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

     model.save_pretrained(lowercase__ )

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:					int						=					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__		:					Union[str, 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,
      )




import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      SCREAMING_SNAKE_CASE__		:					Any						=					False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor

class       snake_case					(      unittest.TestCase						):




  def __init__(      self     :	List[Any]     , a_     :	Optional[int]     , a_     :	Dict=7     , a_     :	Any=3     , a_     :	Any=18     , a_     :	int=30     , a_     :	int=400     , a_     :	List[Any]=None     , a_     :	int=True     , a_     :	int=True     , a_     :	Dict=None     , )->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		size if size is not None else {'height': 20, 'width': 20}
       SCREAMING_SNAKE_CASE__				:				str			=		parent
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		batch_size
       SCREAMING_SNAKE_CASE__				:				Any			=		num_channels
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image_size
       SCREAMING_SNAKE_CASE__				:				List[str]			=		min_resolution
       SCREAMING_SNAKE_CASE__				:				Dict			=		max_resolution
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		do_normalize
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		do_convert_rgb
       SCREAMING_SNAKE_CASE__				:				List[str]			=		[512, 1024, 2048, 4096]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		patch_size if patch_size is not None else {'height': 16, 'width': 16}




  def 	__lowercase(      self     :	Optional[Any]				)->       str:




       """simple docstring"""
       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




  def 	__lowercase(      self     :	Dict				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
       SCREAMING_SNAKE_CASE__				:				str			=		Image.open(requests.get(a_     , stream=a_				).raw				).convert('RGB'				)
       return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'   ,						)
@require_torch
@require_vision
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PixaStructImageProcessor if is_vision_available() else None




  def 	__lowercase(      self     :	List[str]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		PixaStructImageProcessingTester(self				)




  @property
  def 	__lowercase(      self     :	Dict				)->       Tuple:




       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(a_     , 'do_normalize'				)				)
       self.assertTrue(hasattr(a_     , 'do_convert_rgb'				)				)




  def 	__lowercase(      self     :	List[Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.image_processor_tester.prepare_dummy_image()

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processing_class(**self.image_processor_dict				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		2048

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(a_     , return_tensors='pt'     , max_patches=a_				)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     , torch.tensor(0.0606				)     , atol=1e-3     , rtol=1e-3				)				)




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Dict			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				List[str]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Tuple			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	Any				)->       Any:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Dict			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       SCREAMING_SNAKE_CASE__				:				int			=		True

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            with self.assertRaises(a_				):
                 SCREAMING_SNAKE_CASE__				:				Dict			=		image_processor(
                     image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		'Hello'

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_     , header_text=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Any			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_     , header_text=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	List[Any]				)->       Dict:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_     , numpify=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , np.ndarray				)

       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				str			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				int			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	str				)->       Optional[Any]:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Any			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_     , torchify=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , torch.Tensor				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Any			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'   ,						)
@require_torch
@require_vision
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PixaStructImageProcessor if is_vision_available() else None




  def 	__lowercase(      self     :	Union[str, Any]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		PixaStructImageProcessingTester(self     , num_channels=4				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		3




  @property
  def 	__lowercase(      self     :	Any				)->       Any:




       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()




  def 	__lowercase(      self     :	Dict				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(a_     , 'do_normalize'				)				)
       self.assertTrue(hasattr(a_     , 'do_convert_rgb'				)				)




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




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				str			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Tuple			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Dict			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Tuple			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )



