


import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_A									=		logging.getLogger(__name__)

_A									=		int(1_0000)  # Hardcoded max length to avoid infinite loop

_A									=		{
    'gpt2': (GPTaLMHeadModel, GPTaTokenizer),
    'ctrl': (CTRLLMHeadModel, CTRLTokenizer),
    'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
    'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
    'xlm': (XLMWithLMHeadModel, XLMTokenizer),
    'gptj': (GPTJForCausalLM, AutoTokenizer),
    'bloom': (BloomForCausalLM, BloomTokenizerFast),
    'llama': (LlamaForCausalLM, LlamaTokenizer),
    'opt': (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
_A									=		'In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>'

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Union[str, Any]							):
     np.random.seed(args.seed							)
     torch.manual_seed(args.seed							)
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Dict     ,	SCREAMING_SNAKE_CASE__     :   str     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]							):
     if args.temperature > 0.7:
          logger.info('CTRL typically works better with lower temperatures (and lower top_k).'							)

     __UpperCamelCase       				=tokenizer.encode(SCREAMING_SNAKE_CASE__     ,	add_special_tokens=SCREAMING_SNAKE_CASE__							)
     if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()							):
          logger.info('WARNING! You are not starting your generation from a control code so you won\'t get good results'							)
     return prompt_text

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   str     ,	SCREAMING_SNAKE_CASE__     :   str     ,	SCREAMING_SNAKE_CASE__     :   Dict     ,	SCREAMING_SNAKE_CASE__     :   List[Any]							):
     # kwargs = {"language": None, "mask_token_id": None}

     # Set the language
     __UpperCamelCase       				=hasattr(model.config     ,	'use_lang_emb'							) and model.config.use_lang_emb
     if hasattr(model.config     ,	'lang2id'							) and use_lang_emb:
          __UpperCamelCase       				=model.config.langaid.keys()
          if args.xlm_language in available_languages:
               __UpperCamelCase       				=args.xlm_language
          else:
               __UpperCamelCase       				=None
               while language not in available_languages:
                    __UpperCamelCase       				=input('Using XLM. Select language in ' + str(list(SCREAMING_SNAKE_CASE__							)							) + ' >>> '							)

          __UpperCamelCase       				=model.config.langaid[language]
          # kwargs["language"] = tokenizer.lang2id[language]

     # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
     # XLM masked-language modeling (MLM) models need masked token
     # is_xlm_mlm = "mlm" in args.model_name_or_path
     # if is_xlm_mlm:
     #     kwargs["mask_token_id"] = tokenizer.mask_token_id

     return prompt_text

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[int]     ,	SCREAMING_SNAKE_CASE__     :   Optional[int]     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   List[Any]							):
     __UpperCamelCase       				=args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
     __UpperCamelCase       				=prefix + prompt_text
     return prompt_text

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Dict     ,	SCREAMING_SNAKE_CASE__     :   Dict     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Optional[int]							):
     __UpperCamelCase       				=args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
     __UpperCamelCase       				=prefix + prompt_text
     return prompt_text


_A									=		{
    'ctrl': prepare_ctrl_input,
    'xlm': prepare_xlm_input,
    'xlnet': prepare_xlnet_input,
    'transfo-xl': prepare_transfoxl_input,
}

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     if length < 0 and max_sequence_length > 0:
          __UpperCamelCase       				=max_sequence_length
     elif 0 < max_sequence_length < length:
          __UpperCamelCase       				=max_sequence_length  # No generation bigger than model size
     elif length < 0:
          __UpperCamelCase       				=MAX_LENGTH  # avoid infinite loop
     return length

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]							):
     __UpperCamelCase       				=None
     if hasattr(SCREAMING_SNAKE_CASE__     ,	'hidden_size'							):
          __UpperCamelCase       				=model_config.hidden_size
     elif hasattr(SCREAMING_SNAKE_CASE__     ,	'n_embed'							):
          __UpperCamelCase       				=model_config.n_embed
     elif hasattr(SCREAMING_SNAKE_CASE__     ,	'n_embd'							):
          __UpperCamelCase       				=model_config.n_embd

     __UpperCamelCase       				=None
     if hasattr(SCREAMING_SNAKE_CASE__     ,	'num_attention_heads'							):
          __UpperCamelCase       				=model_config.num_attention_heads
     elif hasattr(SCREAMING_SNAKE_CASE__     ,	'n_head'							):
          __UpperCamelCase       				=model_config.n_head

     if embedding_size is None or num_head is None or num_head == 0:
          raise ValueError('Check the model config'							)

     __UpperCamelCase       				=int(embedding_size / num_head							)
     if hasattr(SCREAMING_SNAKE_CASE__     ,	'n_layer'							):
          __UpperCamelCase       				=model_config.n_layer
     elif hasattr(SCREAMING_SNAKE_CASE__     ,	'num_hidden_layers'							):
          __UpperCamelCase       				=model_config.num_hidden_layers
     else:
          raise ValueError('Number of hidden layers couldn\'t be determined from the model config'							)

     return num_layer, num_head, num_embedding_size_per_head

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Dict     ,	SCREAMING_SNAKE_CASE__     :   Optional[int]     ,	SCREAMING_SNAKE_CASE__     :   List[Any]							):
     __UpperCamelCase				, __UpperCamelCase				, __UpperCamelCase       				=sparse_model_config(model.config							)
     if model.config.model_type == "bloom":
          __UpperCamelCase       				=tuple(
              (
                  torch.empty(int(num_attention_heads * batch_size							)     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
                  .to(model.dtype							)
                  .to(model.device							),
                  torch.empty(int(num_attention_heads * batch_size							)     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
                  .to(model.dtype							)
                  .to(model.device							),
              )
              for _ in range(SCREAMING_SNAKE_CASE__							)							)
     else:
          __UpperCamelCase       				=tuple(
              (
                  torch.empty(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
                  .to(model.dtype							)
                  .to(model.device							),
                  torch.empty(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
                  .to(model.dtype							)
                  .to(model.device							),
              )
              for _ in range(SCREAMING_SNAKE_CASE__							)							)
     return past_key_values

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   str     ,	SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     __UpperCamelCase       				=len(SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE__     ,	return_tensors='pt'							)
     __UpperCamelCase       				=dummy_input.to(model.device							)
     if model.config.use_cache:
          __UpperCamelCase       				=generate_past_key_values(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	1							)
     __UpperCamelCase       				=torch.cat(
         [
             torch.zeros(dummy_input['attention_mask'].shape[0]     ,	1							)
             .to(dummy_input['attention_mask'].dtype							)
             .to(model.device							),
             dummy_input['attention_mask'],
         ]     ,	-1     ,	)
     return dummy_input






class        UpperCAmelCase__			(   A_						):



    """simple docstring"""






    UpperCAmelCase__		:						Any    =    ("_optimized", "_default")






    def __init__(     self   ,  A_   ,  A_       )    ->			List[Any]:
         __UpperCamelCase       				=optimized
         __UpperCamelCase       				=default






    def __call__(     self   ,  *A_   ,  **A_       )    ->			str:
         if kwargs["past_key_values"] is None and self._default.config.use_cache:
              __UpperCamelCase       				=generate_past_key_values(self._default   ,  kwargs['input_ids'].shape[0]   ,  0       )
         kwargs.pop('position_ids'   ,  A_       )
         for k in list(kwargs.keys()       ):
              if kwargs[k] is None or isinstance(kwargs[k]   ,  A_       ):
                   kwargs.pop(A_       )
         __UpperCamelCase       				=self._optimized(**A_       )
         __UpperCamelCase       				=outputs[0]
         __UpperCamelCase       				=outputs[1]
         __UpperCamelCase       				=CausalLMOutputWithPast(
             loss=A_   ,  logits=A_   ,  past_key_values=A_   ,  hidden_states=A_   ,  attentions=A_   ,  )
         return fixed_output






    def __getattr__(     self   ,  A_       )    ->			Tuple:
         return getattr(self._default   ,  A_       )






    def _a   (     self   ,  A_   ,  A_=None   ,  A_=None   ,  A_=None   ,  **A_       )    ->			Optional[Any]:
         return self._default.prepare_inputs_for_generation(
             A_   ,  past_key_values=A_   ,  inputs_embeds=A_   ,  use_cache=A_   ,  **A_       )






    def _a   (     self   ,  A_   ,  A_       )    ->			Tuple[Tuple[torch.Tensor]]:
         return self._default._reorder_cache(A_   ,  A_       )

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				=argparse.ArgumentParser()
     parser.add_argument(
         '--model_type'     ,	default=SCREAMING_SNAKE_CASE__     ,	type=SCREAMING_SNAKE_CASE__     ,	required=SCREAMING_SNAKE_CASE__     ,	help='Model type selected in the list: ' + ', '.join(MODEL_CLASSES.keys()							)     ,	)
     parser.add_argument(
         '--model_name_or_path'     ,	default=SCREAMING_SNAKE_CASE__     ,	type=SCREAMING_SNAKE_CASE__     ,	required=SCREAMING_SNAKE_CASE__     ,	help='Path to pre-trained model or shortcut name selected in the list: ' + ', '.join(MODEL_CLASSES.keys()							)     ,	)

     parser.add_argument('--prompt'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''							)
     parser.add_argument('--length'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=20							)
     parser.add_argument('--stop_token'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=SCREAMING_SNAKE_CASE__     ,	help='Token at which text generation is stopped'							)

     parser.add_argument(
         '--temperature'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=1.0     ,	help='temperature of 1.0 has no effect, lower tend toward greedy sampling'     ,	)
     parser.add_argument(
         '--repetition_penalty'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=1.0     ,	help='primarily useful for CTRL model; in that case, use 1.2'							)
     parser.add_argument('--k'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0							)
     parser.add_argument('--p'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0.9							)

     parser.add_argument('--prefix'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Text added prior to input.'							)
     parser.add_argument('--padding_text'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Deprecated, the use of `--prefix` is preferred.'							)
     parser.add_argument('--xlm_language'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Optional language when used with the XLM model.'							)

     parser.add_argument('--seed'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=42     ,	help='random seed for initialization'							)
     parser.add_argument('--no_cuda'     ,	action='store_true'     ,	help='Avoid using CUDA when available'							)
     parser.add_argument('--num_return_sequences'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=1     ,	help='The number of samples to generate.'							)
     parser.add_argument(
         '--fp16'     ,	action='store_true'     ,	help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'     ,	)
     parser.add_argument('--jit'     ,	action='store_true'     ,	help='Whether or not to use jit trace to accelerate inference'							)
     __UpperCamelCase       				=parser.parse_args()

     __UpperCamelCase       				=torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
     __UpperCamelCase       				=0 if args.no_cuda else torch.cuda.device_count()

     logger.warning(F'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'							)

     set_seed(SCREAMING_SNAKE_CASE__							)

     # Initialize the model and tokenizer
     try:
          __UpperCamelCase       				=args.model_type.lower()
          __UpperCamelCase				, __UpperCamelCase       				=MODEL_CLASSES[args.model_type]
     except KeyError:
          raise KeyError('the model {} you specified is not supported. You are welcome to add it and open a PR :)'							)

     __UpperCamelCase       				=tokenizer_class.from_pretrained(args.model_name_or_path							)
     if tokenizer.pad_token is None:
          __UpperCamelCase       				=tokenizer.eos_token
     __UpperCamelCase       				=model_class.from_pretrained(args.model_name_or_path							)
     model.to(args.device							)

     if args.fpaa:
          model.half()
     __UpperCamelCase       				=getattr(model.config     ,	'max_position_embeddings'     ,	0							)
     __UpperCamelCase       				=adjust_length_to_model(args.length     ,	max_sequence_length=SCREAMING_SNAKE_CASE__							)
     logger.info(SCREAMING_SNAKE_CASE__							)

     __UpperCamelCase       				=args.prompt if args.prompt else input('Model prompt >>> '							)

     # Different models need different input formatting and/or extra arguments
     __UpperCamelCase       				=args.model_type in PREPROCESSING_FUNCTIONS.keys()
     if requires_preprocessing:
          __UpperCamelCase       				=PREPROCESSING_FUNCTIONS.get(args.model_type							)
          __UpperCamelCase       				=prepare_input(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

          if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
               __UpperCamelCase       				={'add_space_before_punct_symbol': True}
          else:
               __UpperCamelCase       				={}

          __UpperCamelCase       				=tokenizer.encode(
              SCREAMING_SNAKE_CASE__     ,	add_special_tokens=SCREAMING_SNAKE_CASE__     ,	return_tensors='pt'     ,	**SCREAMING_SNAKE_CASE__							)
     else:
          __UpperCamelCase       				=args.prefix if args.prefix else args.padding_text
          __UpperCamelCase       				=tokenizer.encode(prefix + prompt_text     ,	add_special_tokens=SCREAMING_SNAKE_CASE__     ,	return_tensors='pt'							)
     __UpperCamelCase       				=encoded_prompt.to(args.device							)

     if encoded_prompt.size()[-1] == 0:
          __UpperCamelCase       				=None
     else:
          __UpperCamelCase       				=encoded_prompt

     if args.jit:
          __UpperCamelCase       				=['enable jit']
          __UpperCamelCase       				=prepare_jit_inputs(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
          torch._C._jit_set_texpr_fuser_enabled(SCREAMING_SNAKE_CASE__							)
          __UpperCamelCase       				=False
          if hasattr(SCREAMING_SNAKE_CASE__     ,	'forward'							):
               __UpperCamelCase       				=inspect.signature(model.forward							)
          else:
               __UpperCamelCase       				=inspect.signature(model.__call__							)
          __UpperCamelCase       				=tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							) is not None							)
          __UpperCamelCase       				=torch.jit.trace(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	strict=SCREAMING_SNAKE_CASE__							)
          __UpperCamelCase       				=torch.jit.freeze(traced_model.eval()							)
          traced_model(*SCREAMING_SNAKE_CASE__							)
          traced_model(*SCREAMING_SNAKE_CASE__							)

          __UpperCamelCase       				=_ModelFallbackWrapper(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     __UpperCamelCase       				=model.generate(
         input_ids=SCREAMING_SNAKE_CASE__     ,	max_length=args.length + len(encoded_prompt[0]							)     ,	temperature=args.temperature     ,	top_k=args.k     ,	top_p=args.p     ,	repetition_penalty=args.repetition_penalty     ,	do_sample=SCREAMING_SNAKE_CASE__     ,	num_return_sequences=args.num_return_sequences     ,	)

     # Remove the batch dimension when returning multiple sequences
     if len(output_sequences.shape							) > 2:
          output_sequences.squeeze_()

     __UpperCamelCase       				=[]

     for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE__							):
          print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='							)
          __UpperCamelCase       				=generated_sequence.tolist()

          # Decode text
          __UpperCamelCase       				=tokenizer.decode(SCREAMING_SNAKE_CASE__     ,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__							)

          # Remove all text after the stop token
          __UpperCamelCase       				=text[: text.find(args.stop_token							) if args.stop_token else None]

          # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
          __UpperCamelCase       				=(
              prompt_text + text[len(tokenizer.decode(encoded_prompt[0]     ,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__							)							) :]
          )

          generated_sequences.append(SCREAMING_SNAKE_CASE__							)
          print(SCREAMING_SNAKE_CASE__							)

     return generated_sequences


if __name__ == "__main__":
   main()









import unittest

import numpy as np

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

if is_vision_available():
   from PIL import Image

   from transformers import EfficientNetImageProcessor






class        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_=13   ,  A_=3   ,  A_=18   ,  A_=30   ,  A_=400   ,  A_=True   ,  A_=None   ,  A_=True   ,  A_=[0.5, 0.5, 0.5]   ,  A_=[0.5, 0.5, 0.5]   ,  )    ->			int:
         __UpperCamelCase       				=size if size is not None else {'height': 18, 'width': 18}
         __UpperCamelCase       				=parent
         __UpperCamelCase       				=batch_size
         __UpperCamelCase       				=num_channels
         __UpperCamelCase       				=image_size
         __UpperCamelCase       				=min_resolution
         __UpperCamelCase       				=max_resolution
         __UpperCamelCase       				=do_resize
         __UpperCamelCase       				=size
         __UpperCamelCase       				=do_normalize
         __UpperCamelCase       				=image_mean
         __UpperCamelCase       				=image_std






    def _a   (     self       )    ->			Optional[Any]:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }






@require_torch
@require_vision
class        UpperCAmelCase__			(   A_  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						List[Any]    =    EfficientNetImageProcessor if is_vision_available() else None






    def _a   (     self       )    ->			List[str]:
         __UpperCamelCase       				=EfficientNetImageProcessorTester(self       )






    @property
    def _a   (     self       )    ->			Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()






    def _a   (     self       )    ->			str:
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(A_   ,  'image_mean'       )       )
         self.assertTrue(hasattr(A_   ,  'image_std'       )       )
         self.assertTrue(hasattr(A_   ,  'do_normalize'       )       )
         self.assertTrue(hasattr(A_   ,  'do_resize'       )       )
         self.assertTrue(hasattr(A_   ,  'size'       )       )






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size   ,  {'height': 18, 'width': 18}       )

         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict   ,  size=42       )
         self.assertEqual(image_processor.size   ,  {'height': 42, 'width': 42}       )






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  Image.Image       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  numpify=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  np.ndarray       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )






    def _a   (     self       )    ->			Dict:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         __UpperCamelCase       				=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
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )





