code: 
  
  
  """simple docstring"""
  
  
  
  
  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,
  )
  __magic_name__	:   Any          =							logging.getLogger(__name__)
  
  __magic_name__	:   str          =							int(1_0_0_0_0)  # Hardcoded max length to avoid infinite loop
  
  __magic_name__	:   List[Any]          =							{
      '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
  __magic_name__	:   Any          =							'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 			a_   (  lowercase__   :Optional[Any]    ):
  			np.random.seed(args.seed    )
  			torch.manual_seed(args.seed    )
  			if args.n_gpu > 0:
  						torch.cuda.manual_seed_all(args.seed    )
  
  
  
  
  
  def 			a_   (  lowercase__   :List[str], lowercase__   :Tuple, lowercase__   :Optional[int], lowercase__   :Dict    ):
  			if args.temperature > 0.7:
  						logger.info("""CTRL typically works better with lower temperatures (and lower top_k)."""    )
  
  			__lowerCamelCase     :   int        =			tokenizer.encode(UpperCamelCase__, add_special_tokens=UpperCamelCase__    )
  			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 			a_   (  lowercase__   :Optional[Any], lowercase__   :List[str], lowercase__   :Optional[int], lowercase__   :List[str]    ):
  			# kwargs = {"language": None, "mask_token_id": None}
  
  			# Set the language
  			__lowerCamelCase     :   Union[str, Any]        =			hasattr(model.config, """use_lang_emb"""    ) and model.config.use_lang_emb
  			if hasattr(model.config, """lang2id"""    ) and use_lang_emb:
  						__lowerCamelCase     :   Optional[Any]        =			model.config.langaid.keys()
  						if args.xlm_language in available_languages:
  									__lowerCamelCase     :   Tuple        =			args.xlm_language
  						else:
  									__lowerCamelCase     :   Optional[int]        =			None
  									while language not in available_languages:
  												__lowerCamelCase     :   Tuple        =			input("""Using XLM. Select language in """ + str(list(UpperCamelCase__    )    ) + """ >>> """    )
  
  						__lowerCamelCase     :   List[Any]        =			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 			a_   (  lowercase__   :Optional[Any], lowercase__   :Any, lowercase__   :Dict, lowercase__   :List[Any]    ):
  			__lowerCamelCase     :   List[Any]        =			args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
  			__lowerCamelCase     :   Optional[int]        =			prefix + prompt_text
  			return prompt_text
  
  
  
  
  
  def 			a_   (  lowercase__   :Union[str, Any], lowercase__   :List[str], lowercase__   :List[str], lowercase__   :Tuple    ):
  			__lowerCamelCase     :   Optional[Any]        =			args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
  			__lowerCamelCase     :   Any        =			prefix + prompt_text
  			return prompt_text
  
  
  __magic_name__	:   int          =							{
      'ctrl': prepare_ctrl_input,
      'xlm': prepare_xlm_input,
      'xlnet': prepare_xlnet_input,
      'transfo-xl': prepare_transfoxl_input,
  }
  
  
  
  
  
  def 			a_   (  lowercase__   :Dict, lowercase__   :int    ):
  			if length < 0 and max_sequence_length > 0:
  						__lowerCamelCase     :   Optional[int]        =			max_sequence_length
  			elif 0 < max_sequence_length < length:
  						__lowerCamelCase     :   Tuple        =			max_sequence_length  # No generation bigger than model size
  			elif length < 0:
  						__lowerCamelCase     :   Optional[Any]        =			MAX_LENGTH  # avoid infinite loop
  			return length
  
  
  
  
  
  def 			a_   (  lowercase__   :Tuple    ):
  			__lowerCamelCase     :   Dict        =			None
  			if hasattr(UpperCamelCase__, """hidden_size"""    ):
  						__lowerCamelCase     :   str        =			model_config.hidden_size
  			elif hasattr(UpperCamelCase__, """n_embed"""    ):
  						__lowerCamelCase     :   List[str]        =			model_config.n_embed
  			elif hasattr(UpperCamelCase__, """n_embd"""    ):
  						__lowerCamelCase     :   Optional[Any]        =			model_config.n_embd
  
  			__lowerCamelCase     :   Optional[Any]        =			None
  			if hasattr(UpperCamelCase__, """num_attention_heads"""    ):
  						__lowerCamelCase     :   Tuple        =			model_config.num_attention_heads
  			elif hasattr(UpperCamelCase__, """n_head"""    ):
  						__lowerCamelCase     :   Any        =			model_config.n_head
  
  			if embedding_size is None or num_head is None or num_head == 0:
  						raise ValueError("""Check the model config"""    )
  
  			__lowerCamelCase     :   int        =			int(embedding_size / num_head    )
  			if hasattr(UpperCamelCase__, """n_layer"""    ):
  						__lowerCamelCase     :   Tuple        =			model_config.n_layer
  			elif hasattr(UpperCamelCase__, """num_hidden_layers"""    ):
  						__lowerCamelCase     :   str        =			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 			a_   (  lowercase__   :Optional[int], lowercase__   :Dict, lowercase__   :Tuple    ):
  			__lowerCamelCase     :   Optional[int]        =			sparse_model_config(model.config    )
  			if model.config.model_type == "bloom":
  						__lowerCamelCase     :   Any        =			tuple(
  						    (
  						        torch.empty(int(num_attention_heads * batch_size    ), UpperCamelCase__, UpperCamelCase__    )
  						        .to(model.dtype    )
  						        .to(model.device    ),
  						        torch.empty(int(num_attention_heads * batch_size    ), UpperCamelCase__, UpperCamelCase__    )
  						        .to(model.dtype    )
  						        .to(model.device    ),
  						    )
  						    for _ in range(UpperCamelCase__    )    )
  			else:
  						__lowerCamelCase     :   Union[str, Any]        =			tuple(
  						    (
  						        torch.empty(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__    )
  						        .to(model.dtype    )
  						        .to(model.device    ),
  						        torch.empty(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__    )
  						        .to(model.dtype    )
  						        .to(model.device    ),
  						    )
  						    for _ in range(UpperCamelCase__    )    )
  			return past_key_values
  
  
  
  
  
  def 			a_   (  lowercase__   :Optional[Any], lowercase__   :List[str], lowercase__   :Any    ):
  			__lowerCamelCase     :   List[str]        =			len(UpperCamelCase__    )
  			__lowerCamelCase     :   Tuple        =			tokenizer.batch_encode_plus(UpperCamelCase__, return_tensors="""pt"""    )
  			__lowerCamelCase     :   Any        =			dummy_input.to(model.device    )
  			if model.config.use_cache:
  						__lowerCamelCase     :   Tuple        =			generate_past_key_values(UpperCamelCase__, UpperCamelCase__, 1    )
  			__lowerCamelCase     :   Any        =			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  __snake_case      (lowerCamelCase     ):
  							__a				   =					('''_optimized''', '''_default''')
  
  
  
  
  							def __init__(    self:						Any    ,       A_:						Tuple    ,       A_:						Dict      ):
  										__lowerCamelCase     :   Optional[int]        =			optimized
  										__lowerCamelCase     :   Optional[Any]        =			default
  
  
  
  
  							def __call__(    self:						Optional[Any]    ,       *A_:						str    ,       **A_:						int      ):
  										if kwargs["past_key_values"] is None and self._default.config.use_cache:
  													__lowerCamelCase     :   Optional[Any]        =			generate_past_key_values(self._default    ,       kwargs["""input_ids"""].shape[0]    ,       0      )
  										kwargs.pop("""position_ids"""    ,       __lowerCamelCase      )
  										for k in list(kwargs.keys()      ):
  													if kwargs[k] is None or isinstance(kwargs[k]    ,       __lowerCamelCase      ):
  																kwargs.pop(__lowerCamelCase      )
  										__lowerCamelCase     :   List[Any]        =			self._optimized(**__lowerCamelCase      )
  										__lowerCamelCase     :   Any        =			outputs[0]
  										__lowerCamelCase     :   Any        =			outputs[1]
  										__lowerCamelCase     :   Tuple        =			CausalLMOutputWithPast(
  										    loss=__lowerCamelCase    ,       logits=__lowerCamelCase    ,       past_key_values=__lowerCamelCase    ,       hidden_states=__lowerCamelCase    ,       attentions=__lowerCamelCase    ,       )
  										return fixed_output
  
  
  
  
  							def __getattr__(    self:						Optional[Any]    ,       A_:						List[Any]      ):
  										return getattr(self._default    ,       __lowerCamelCase      )
  
  
  
  
  							def      __a      (    self:						Optional[int]    ,       A_:						str    ,       A_:						List[str]=None    ,       A_:						List[str]=None    ,       A_:						str=None    ,       **A_:						Optional[Any]      ):
  										return self._default.prepare_inputs_for_generation(
  										    __lowerCamelCase    ,       past_key_values=__lowerCamelCase    ,       inputs_embeds=__lowerCamelCase    ,       use_cache=__lowerCamelCase    ,       **__lowerCamelCase      )
  
  
  
  
  							def      __a      (    self:						Dict    ,       A_:						Tuple    ,       A_:						Optional[Any]      ):
  										return self._default._reorder_cache(__lowerCamelCase    ,       __lowerCamelCase      )
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase     :   Union[str, Any]        =			argparse.ArgumentParser()
  			parser.add_argument(
  			    """--model_type""", default=UpperCamelCase__, type=UpperCamelCase__, required=UpperCamelCase__, help="""Model type selected in the list: """ + """, """.join(MODEL_CLASSES.keys()    ), )
  			parser.add_argument(
  			    """--model_name_or_path""", default=UpperCamelCase__, type=UpperCamelCase__, required=UpperCamelCase__, help="""Path to pre-trained model or shortcut name selected in the list: """ + """, """.join(MODEL_CLASSES.keys()    ), )
  
  			parser.add_argument("""--prompt""", type=UpperCamelCase__, default=""""""    )
  			parser.add_argument("""--length""", type=UpperCamelCase__, default=20    )
  			parser.add_argument("""--stop_token""", type=UpperCamelCase__, default=UpperCamelCase__, help="""Token at which text generation is stopped"""    )
  
  			parser.add_argument(
  			    """--temperature""", type=UpperCamelCase__, default=1.0, help="""temperature of 1.0 has no effect, lower tend toward greedy sampling""", )
  			parser.add_argument(
  			    """--repetition_penalty""", type=UpperCamelCase__, default=1.0, help="""primarily useful for CTRL model; in that case, use 1.2"""    )
  			parser.add_argument("""--k""", type=UpperCamelCase__, default=0    )
  			parser.add_argument("""--p""", type=UpperCamelCase__, default=0.9    )
  
  			parser.add_argument("""--prefix""", type=UpperCamelCase__, default="""""", help="""Text added prior to input."""    )
  			parser.add_argument("""--padding_text""", type=UpperCamelCase__, default="""""", help="""Deprecated, the use of `--prefix` is preferred."""    )
  			parser.add_argument("""--xlm_language""", type=UpperCamelCase__, default="""""", help="""Optional language when used with the XLM model."""    )
  
  			parser.add_argument("""--seed""", type=UpperCamelCase__, 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=UpperCamelCase__, 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"""    )
  			__lowerCamelCase     :   Union[str, Any]        =			parser.parse_args()
  
  			__lowerCamelCase     :   List[str]        =			torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""    )
  			__lowerCamelCase     :   Any        =			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(UpperCamelCase__    )
  
  			# Initialize the model and tokenizer
  			try:
  						__lowerCamelCase     :   Tuple        =			args.model_type.lower()
  						__lowerCamelCase     :   str        =			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 :)"""    )
  
  			__lowerCamelCase     :   Tuple        =			tokenizer_class.from_pretrained(args.model_name_or_path    )
  			if tokenizer.pad_token is None:
  						__lowerCamelCase     :   str        =			tokenizer.eos_token
  			__lowerCamelCase     :   Union[str, Any]        =			model_class.from_pretrained(args.model_name_or_path    )
  			model.to(args.device    )
  
  			if args.fpaa:
  						model.half()
  			__lowerCamelCase     :   Dict        =			getattr(model.config, """max_position_embeddings""", 0    )
  			__lowerCamelCase     :   List[Any]        =			adjust_length_to_model(args.length, max_sequence_length=UpperCamelCase__    )
  			logger.info(UpperCamelCase__    )
  
  			__lowerCamelCase     :   List[Any]        =			args.prompt if args.prompt else input("""Model prompt >>> """    )
  
  			# Different models need different input formatting and/or extra arguments
  			__lowerCamelCase     :   List[str]        =			args.model_type in PREPROCESSING_FUNCTIONS.keys()
  			if requires_preprocessing:
  						__lowerCamelCase     :   List[Any]        =			PREPROCESSING_FUNCTIONS.get(args.model_type    )
  						__lowerCamelCase     :   Tuple        =			prepare_input(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__    )
  
  						if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
  									__lowerCamelCase     :   Tuple        =			{"add_space_before_punct_symbol": True}
  						else:
  									__lowerCamelCase     :   Dict        =			{}
  
  						__lowerCamelCase     :   List[Any]        =			tokenizer.encode(
  						    UpperCamelCase__, add_special_tokens=UpperCamelCase__, return_tensors="""pt""", **UpperCamelCase__    )
  			else:
  						__lowerCamelCase     :   int        =			args.prefix if args.prefix else args.padding_text
  						__lowerCamelCase     :   Any        =			tokenizer.encode(prefix + prompt_text, add_special_tokens=UpperCamelCase__, return_tensors="""pt"""    )
  			__lowerCamelCase     :   str        =			encoded_prompt.to(args.device    )
  
  			if encoded_prompt.size()[-1] == 0:
  						__lowerCamelCase     :   Union[str, Any]        =			None
  			else:
  						__lowerCamelCase     :   Any        =			encoded_prompt
  
  			if args.jit:
  						__lowerCamelCase     :   Union[str, Any]        =			["enable jit"]
  						__lowerCamelCase     :   List[Any]        =			prepare_jit_inputs(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__    )
  						torch._C._jit_set_texpr_fuser_enabled(UpperCamelCase__    )
  						__lowerCamelCase     :   Union[str, Any]        =			False
  						if hasattr(UpperCamelCase__, """forward"""    ):
  									__lowerCamelCase     :   List[Any]        =			inspect.signature(model.forward    )
  						else:
  									__lowerCamelCase     :   Union[str, Any]        =			inspect.signature(model.__call__    )
  						__lowerCamelCase     :   str        =			tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(UpperCamelCase__, UpperCamelCase__    ) is not None    )
  						__lowerCamelCase     :   Any        =			torch.jit.trace(UpperCamelCase__, UpperCamelCase__, strict=UpperCamelCase__    )
  						__lowerCamelCase     :   Dict        =			torch.jit.freeze(traced_model.eval()    )
  						traced_model(*UpperCamelCase__    )
  						traced_model(*UpperCamelCase__    )
  
  						__lowerCamelCase     :   int        =			_ModelFallbackWrapper(UpperCamelCase__, UpperCamelCase__    )
  
  			__lowerCamelCase     :   Union[str, Any]        =			model.generate(
  			    input_ids=UpperCamelCase__, 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=UpperCamelCase__, num_return_sequences=args.num_return_sequences, )
  
  			# Remove the batch dimension when returning multiple sequences
  			if len(output_sequences.shape    ) > 2:
  						output_sequences.squeeze_()
  
  			__lowerCamelCase     :   List[str]        =			[]
  
  			for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__    ):
  						print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='    )
  						__lowerCamelCase     :   Optional[Any]        =			generated_sequence.tolist()
  
  						# Decode text
  						__lowerCamelCase     :   List[Any]        =			tokenizer.decode(UpperCamelCase__, clean_up_tokenization_spaces=UpperCamelCase__    )
  
  						# Remove all text after the stop token
  						__lowerCamelCase     :   int        =			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
  						__lowerCamelCase     :   int        =			(
  						    prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=UpperCamelCase__    )    ) :]
  						)
  
  						generated_sequences.append(UpperCamelCase__    )
  						print(UpperCamelCase__    )
  
  			return generated_sequences
  
  
  if __name__ == "__main__":
  					main()
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import inspect
  import unittest
  
  from transformers import SwinaSRConfig
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  					import torch
  					from torch import nn
  
  					from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
  					from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST
  
  if is_vision_available():
  					from PIL import Image
  
  					from transformers import SwinaSRImageProcessor
  
  
  
  
  class  __snake_case      :
  
  
  
  
  							def __init__(    self:						str    ,       A_:						str    ,       A_:						int=13    ,       A_:						Any=32    ,       A_:						int=1    ,       A_:						Dict=3    ,       A_:						Union[str, Any]=16    ,       A_:						List[str]=[1, 2, 1]    ,       A_:						Dict=[2, 2, 4]    ,       A_:						List[Any]=2    ,       A_:						List[Any]=2.0    ,       A_:						str=True    ,       A_:						Dict=0.0    ,       A_:						Optional[Any]=0.0    ,       A_:						Tuple=0.1    ,       A_:						str="gelu"    ,       A_:						Optional[Any]=False    ,       A_:						Dict=True    ,       A_:						Tuple=0.02    ,       A_:						List[Any]=1E-5    ,       A_:						Dict=True    ,       A_:						Any=None    ,       A_:						Optional[Any]=False    ,       A_:						Optional[int]=2    ,       ):
  										__lowerCamelCase        =			parent
  										__lowerCamelCase        =			batch_size
  										__lowerCamelCase        =			image_size
  										__lowerCamelCase        =			patch_size
  										__lowerCamelCase        =			num_channels
  										__lowerCamelCase        =			embed_dim
  										__lowerCamelCase        =			depths
  										__lowerCamelCase        =			num_heads
  										__lowerCamelCase        =			window_size
  										__lowerCamelCase        =			mlp_ratio
  										__lowerCamelCase        =			qkv_bias
  										__lowerCamelCase        =			hidden_dropout_prob
  										__lowerCamelCase        =			attention_probs_dropout_prob
  										__lowerCamelCase        =			drop_path_rate
  										__lowerCamelCase        =			hidden_act
  										__lowerCamelCase        =			use_absolute_embeddings
  										__lowerCamelCase        =			patch_norm
  										__lowerCamelCase        =			layer_norm_eps
  										__lowerCamelCase        =			initializer_range
  										__lowerCamelCase        =			is_training
  										__lowerCamelCase        =			scope
  										__lowerCamelCase        =			use_labels
  										__lowerCamelCase        =			upscale
  
  										# here we set some attributes to make tests pass
  										__lowerCamelCase        =			len(A_      )
  										__lowerCamelCase        =			embed_dim
  										__lowerCamelCase        =			(image_size // patch_size) ** 2
  
  
  
  
  							def      __a      (    self:						Tuple      ):
  										__lowerCamelCase        =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )
  
  										__lowerCamelCase        =			None
  										if self.use_labels:
  													__lowerCamelCase        =			ids_tensor([self.batch_size]    ,       self.type_sequence_label_size      )
  
  										__lowerCamelCase        =			self.get_config()
  
  										return config, pixel_values, labels
  
  
  
  
  							def      __a      (    self:						int      ):
  										return SwinaSRConfig(
  										    image_size=self.image_size    ,       patch_size=self.patch_size    ,       num_channels=self.num_channels    ,       embed_dim=self.embed_dim    ,       depths=self.depths    ,       num_heads=self.num_heads    ,       window_size=self.window_size    ,       mlp_ratio=self.mlp_ratio    ,       qkv_bias=self.qkv_bias    ,       hidden_dropout_prob=self.hidden_dropout_prob    ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,       drop_path_rate=self.drop_path_rate    ,       hidden_act=self.hidden_act    ,       use_absolute_embeddings=self.use_absolute_embeddings    ,       path_norm=self.patch_norm    ,       layer_norm_eps=self.layer_norm_eps    ,       initializer_range=self.initializer_range    ,       upscale=self.upscale    ,       )
  
  
  
  
  							def      __a      (    self:						str    ,       A_:						str    ,       A_:						Optional[int]    ,       A_:						List[str]      ):
  										__lowerCamelCase        =			SwinaSRModel(config=A_      )
  										model.to(A_      )
  										model.eval()
  										__lowerCamelCase        =			model(A_      )
  
  										self.parent.assertEqual(
  										    result.last_hidden_state.shape    ,       (self.batch_size, self.embed_dim, self.image_size, self.image_size)      )
  
  
  
  
  							def      __a      (    self:						Any    ,       A_:						Tuple    ,       A_:						Any    ,       A_:						Dict      ):
  										__lowerCamelCase        =			SwinaSRForImageSuperResolution(A_      )
  										model.to(A_      )
  										model.eval()
  										__lowerCamelCase        =			model(A_      )
  
  										__lowerCamelCase        =			self.image_size * self.upscale
  										self.parent.assertEqual(
  										    result.reconstruction.shape    ,       (self.batch_size, self.num_channels, expected_image_size, expected_image_size)      )
  
  
  
  
  							def      __a      (    self:						Tuple      ):
  										__lowerCamelCase        =			self.prepare_config_and_inputs()
  										__lowerCamelCase    ,__lowerCamelCase    ,__lowerCamelCase        =			config_and_inputs
  										__lowerCamelCase        =			{"""pixel_values""": pixel_values}
  										return config, inputs_dict
  
  
  
  
  @require_torch
  class  __snake_case      (lowerCamelCase ,		lowerCamelCase ,		unittest.TestCase     ):
  							__a				   =					(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
  							__a				   =					{'''feature-extraction''': SwinaSRModel} if is_torch_available() else {}
  
  							__a				   =					False
  							__a				   =					False
  							__a				   =					False
  							__a				   =					False
  							__a				   =					False
  
  
  
  
  							def      __a      (    self:						List[str]      ):
  										__lowerCamelCase        =			SwinaSRModelTester(self      )
  										__lowerCamelCase        =			ConfigTester(self    ,       config_class=A_    ,       embed_dim=37      )
  
  
  
  
  							def      __a      (    self:						Optional[Any]      ):
  										self.config_tester.create_and_test_config_to_json_string()
  										self.config_tester.create_and_test_config_to_json_file()
  										self.config_tester.create_and_test_config_from_and_save_pretrained()
  										self.config_tester.create_and_test_config_with_num_labels()
  										self.config_tester.check_config_can_be_init_without_params()
  										self.config_tester.check_config_arguments_init()
  
  
  
  
  							def      __a      (    self:						Tuple      ):
  										__lowerCamelCase        =			self.model_tester.prepare_config_and_inputs()
  										self.model_tester.create_and_check_model(*A_      )
  
  
  
  
  							def      __a      (    self:						Any      ):
  										__lowerCamelCase        =			self.model_tester.prepare_config_and_inputs()
  										self.model_tester.create_and_check_for_image_super_resolution(*A_      )
  
  
  
  
  							@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""      )
  							def      __a      (    self:						Union[str, Any]      ):
  										pass
  
  
  
  
  							@unittest.skip(reason="""Swin2SR does not use inputs_embeds"""      )
  							def      __a      (    self:						str      ):
  										pass
  
  
  
  
  							@unittest.skip(reason="""Swin2SR does not support training yet"""      )
  							def      __a      (    self:						int      ):
  										pass
  
  
  
  
  							@unittest.skip(reason="""Swin2SR does not support training yet"""      )
  							def      __a      (    self:						Optional[int]      ):
  										pass
  
  
  
  
  							def      __a      (    self:						List[Any]      ):
  										__lowerCamelCase    ,__lowerCamelCase        =			self.model_tester.prepare_config_and_inputs_for_common()
  
  										for model_class in self.all_model_classes:
  													__lowerCamelCase        =			model_class(A_      )
  													self.assertIsInstance(model.get_input_embeddings()    ,       (nn.Module)      )
  													__lowerCamelCase        =			model.get_output_embeddings()
  													self.assertTrue(x is None or isinstance(A_    ,       nn.Linear      )      )
  
  
  
  
  							def      __a      (    self:						Optional[Any]      ):
  										__lowerCamelCase    ,__lowerCamelCase        =			self.model_tester.prepare_config_and_inputs_for_common()
  
  										for model_class in self.all_model_classes:
  													__lowerCamelCase        =			model_class(A_      )
  													__lowerCamelCase        =			inspect.signature(model.forward      )
  													# signature.parameters is an OrderedDict => so arg_names order is deterministic
  													__lowerCamelCase        =			[*signature.parameters.keys()]
  
  													__lowerCamelCase        =			["""pixel_values"""]
  													self.assertListEqual(arg_names[:1]    ,       A_      )
  
  
  
  
  							@slow
  							def      __a      (    self:						List[Any]      ):
  										for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  													__lowerCamelCase        =			SwinaSRModel.from_pretrained(A_      )
  													self.assertIsNotNone(A_      )
  
  
  
  
  							def      __a      (    self:						Optional[int]      ):
  										__lowerCamelCase    ,__lowerCamelCase        =			self.model_tester.prepare_config_and_inputs_for_common()
  
  										__lowerCamelCase        =			_config_zero_init(A_      )
  										for model_class in self.all_model_classes:
  													__lowerCamelCase        =			model_class(config=A_      )
  													for name, param in model.named_parameters():
  																if "logit_scale" in name:
  																			continue
  																if param.requires_grad:
  																			self.assertIn(
  																			    ((param.data.mean() * 1E9).round() / 1E9).item()    ,       [0.0, 1.0]    ,       msg=f'Parameter {name} of model {model_class} seems not properly initialized'    ,       )
  
  
  
  
  							def      __a      (    self:						Any      ):
  										__lowerCamelCase    ,__lowerCamelCase        =			self.model_tester.prepare_config_and_inputs_for_common()
  										__lowerCamelCase        =			True
  
  										for model_class in self.all_model_classes:
  													__lowerCamelCase        =			True
  													__lowerCamelCase        =			False
  													__lowerCamelCase        =			True
  													__lowerCamelCase        =			model_class(A_      )
  													model.to(A_      )
  													model.eval()
  													with torch.no_grad():
  																__lowerCamelCase        =			model(**self._prepare_for_class(A_    ,       A_      )      )
  													__lowerCamelCase        =			outputs.attentions
  													__lowerCamelCase        =			len(self.model_tester.depths      )
  													self.assertEqual(len(A_      )    ,       A_      )
  
  													# check that output_attentions also work using config
  													del inputs_dict["output_attentions"]
  													__lowerCamelCase        =			True
  													__lowerCamelCase        =			config.window_size**2
  													__lowerCamelCase        =			model_class(A_      )
  													model.to(A_      )
  													model.eval()
  													with torch.no_grad():
  																__lowerCamelCase        =			model(**self._prepare_for_class(A_    ,       A_      )      )
  													__lowerCamelCase        =			outputs.attentions
  													self.assertEqual(len(A_      )    ,       A_      )
  
  													self.assertListEqual(
  													    list(attentions[0].shape[-3:]      )    ,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,       )
  													__lowerCamelCase        =			len(A_      )
  
  													# Check attention is always last and order is fine
  													__lowerCamelCase        =			True
  													__lowerCamelCase        =			True
  													__lowerCamelCase        =			model_class(A_      )
  													model.to(A_      )
  													model.eval()
  													with torch.no_grad():
  																__lowerCamelCase        =			model(**self._prepare_for_class(A_    ,       A_      )      )
  
  													self.assertEqual(out_len + 1    ,       len(A_      )      )
  
  													__lowerCamelCase        =			outputs.attentions
  
  													self.assertEqual(len(A_      )    ,       A_      )
  
  													self.assertListEqual(
  													    list(self_attentions[0].shape[-3:]      )    ,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,       )
  
  
  
  
  @require_vision
  @require_torch
  @slow
  class  __snake_case      (unittest.TestCase     ):
  
  
  
  
  							def      __a      (    self:						List[Any]      ):
  										__lowerCamelCase        =			SwinaSRImageProcessor()
  										__lowerCamelCase        =			SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64"""      ).to(A_      )
  
  										__lowerCamelCase        =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
  										__lowerCamelCase        =			processor(images=A_    ,       return_tensors="""pt"""      ).to(A_      )
  
  										# forward pass
  										with torch.no_grad():
  													__lowerCamelCase        =			model(**A_      )
  
  										# verify the logits
  										__lowerCamelCase        =			torch.Size([1, 3, 9_76, 12_96]      )
  										self.assertEqual(outputs.reconstruction.shape    ,       A_      )
  										__lowerCamelCase        =			torch.tensor(
  										    [[0.5_458, 0.5_546, 0.5_638], [0.5_526, 0.5_565, 0.5_651], [0.5_396, 0.5_426, 0.5_621]]      ).to(A_      )
  										self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]    ,       A_    ,       atol=1E-4      )      )
  
  
  
  
  