
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


SCREAMING_SNAKE_CASE__     :    Any      							=					logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class      lowerCAmelCase__  (			__lowercase    ):
  a__ :		torch.FloatTensor
  a__ :		Optional[torch.FloatTensor]   						=	None





def 				__magic_name__				(     __lowerCAmelCase			:		Tuple	,						__lowerCAmelCase			:		str=0.999	,						__lowerCAmelCase			:		str="cosine"	,						)    ->     Any:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__lowerCAmelCase			:		List[Any]							):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__lowerCAmelCase			:		Dict							):
            return math.exp(t * -12.0							)

    else:
        raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''							)

    __lowerCamelCase					      =			[]
    for i in range(__lowerCAmelCase							):
        __lowerCamelCase					      =			i / num_diffusion_timesteps
        __lowerCamelCase					      =			(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(__lowerCAmelCase							) / alpha_bar_fn(__lowerCAmelCase							)	,						__lowerCAmelCase							)							)
    return torch.tensor(__lowerCAmelCase	,						dtype=torch.floataa							)







class      lowerCAmelCase__  (			__lowercase						,			__lowercase    ):
  a__ :		Any   						=	[e.name for e in KarrasDiffusionSchedulers]
  a__ :		Optional[Any]   						=	1






  @register_to_config
  def __init__(	self	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							int = 10_00							,    SCREAMING_SNAKE_CASE__	:							float = 0.0001							,    SCREAMING_SNAKE_CASE__	:							float = 0.02							,    SCREAMING_SNAKE_CASE__	:							str = "linear"							,    SCREAMING_SNAKE_CASE__	:							Optional[Union[np.ndarray, List[float]]] = None							,    SCREAMING_SNAKE_CASE__	:							str = "epsilon"							,    SCREAMING_SNAKE_CASE__	:							str = "linear"							,    SCREAMING_SNAKE_CASE__	:							Optional[bool] = False							,    SCREAMING_SNAKE_CASE__	:							str = "linspace"							,    SCREAMING_SNAKE_CASE__	:							int = 0							,    )  ->							Any:
      if trained_betas is not None:
          __lowerCamelCase					      =			torch.tensor(SCREAMING_SNAKE_CASE__							,    dtype=torch.floataa						)
      elif beta_schedule == "linear":
          __lowerCamelCase					      =			torch.linspace(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    dtype=torch.floataa						)
      elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          __lowerCamelCase					      =			(
              torch.linspace(beta_start**0.5							,    beta_end**0.5							,    SCREAMING_SNAKE_CASE__							,    dtype=torch.floataa						) ** 2
          )
      elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          __lowerCamelCase					      =			betas_for_alpha_bar(SCREAMING_SNAKE_CASE__						)
      else:
          raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''						)

      __lowerCamelCase					      =			1.0 - self.betas
      __lowerCamelCase					      =			torch.cumprod(self.alphas							,    dim=0						)

      __lowerCamelCase					      =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
      __lowerCamelCase					      =			np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
      __lowerCamelCase					      =			torch.from_numpy(SCREAMING_SNAKE_CASE__						)

      # setable values
      __lowerCamelCase					      =			None
      __lowerCamelCase					      =			np.linspace(0							,    num_train_timesteps - 1							,    SCREAMING_SNAKE_CASE__							,    dtype=SCREAMING_SNAKE_CASE__						)[::-1].copy()
      __lowerCamelCase					      =			torch.from_numpy(SCREAMING_SNAKE_CASE__						)
      __lowerCamelCase					      =			False
      __lowerCamelCase					      =			use_karras_sigmas






  @property
  def 				__A (	self	:							Union[str, Any]						)  ->							Optional[Any]:
      # standard deviation of the initial noise distribution
      if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

      return (self.sigmas.max() ** 2 + 1) ** 0.5






  def 				__A (	self	:							int							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    SCREAMING_SNAKE_CASE__	:							Union[float, torch.FloatTensor]						)  ->							torch.FloatTensor:
      if isinstance(SCREAMING_SNAKE_CASE__							,    torch.Tensor						):
          __lowerCamelCase					      =			timestep.to(self.timesteps.device						)
      __lowerCamelCase					      =			(self.timesteps == timestep).nonzero().item()
      __lowerCamelCase					      =			self.sigmas[step_index]

      __lowerCamelCase					      =			sample / ((sigma**2 + 1) ** 0.5)

      __lowerCamelCase					      =			True
      return sample






  def 				__A (	self	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							int							,    SCREAMING_SNAKE_CASE__	:							Union[str, torch.device] = None						)  ->							Tuple:
      __lowerCamelCase					      =			num_inference_steps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
          __lowerCamelCase					      =			np.linspace(0							,    self.config.num_train_timesteps - 1							,    SCREAMING_SNAKE_CASE__							,    dtype=SCREAMING_SNAKE_CASE__						)[
              ::-1
          ].copy()
      elif self.config.timestep_spacing == "leading":
          __lowerCamelCase					      =			self.config.num_train_timesteps // self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          __lowerCamelCase					      =			(np.arange(0							,    SCREAMING_SNAKE_CASE__						) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE__						)
          timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
          __lowerCamelCase					      =			self.config.num_train_timesteps / self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          __lowerCamelCase					      =			(np.arange(self.config.num_train_timesteps							,    0							,    -step_ratio						)).round().copy().astype(SCREAMING_SNAKE_CASE__						)
          timesteps -= 1
      else:
          raise ValueError(
              f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''						)

      __lowerCamelCase					      =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
      __lowerCamelCase					      =			np.log(SCREAMING_SNAKE_CASE__						)

      if self.config.interpolation_type == "linear":
          __lowerCamelCase					      =			np.interp(SCREAMING_SNAKE_CASE__							,    np.arange(0							,    len(SCREAMING_SNAKE_CASE__						)						)							,    SCREAMING_SNAKE_CASE__						)
      elif self.config.interpolation_type == "log_linear":
          __lowerCamelCase					      =			torch.linspace(np.log(sigmas[-1]						)							,    np.log(sigmas[0]						)							,    num_inference_steps + 1						).exp()
      else:
          raise ValueError(
              f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
              ''' \'linear\' or \'log_linear\''''						)

      if self.use_karras_sigmas:
          __lowerCamelCase					      =			self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE__							,    num_inference_steps=self.num_inference_steps						)
          __lowerCamelCase					      =			np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						) for sigma in sigmas]						)

      __lowerCamelCase					      =			np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
      __lowerCamelCase					      =			torch.from_numpy(SCREAMING_SNAKE_CASE__						).to(device=SCREAMING_SNAKE_CASE__						)
      if str(SCREAMING_SNAKE_CASE__						).startswith('''mps'''						):
          # mps does not support float64
          __lowerCamelCase					      =			torch.from_numpy(SCREAMING_SNAKE_CASE__						).to(SCREAMING_SNAKE_CASE__							,    dtype=torch.floataa						)
      else:
          __lowerCamelCase					      =			torch.from_numpy(SCREAMING_SNAKE_CASE__						).to(device=SCREAMING_SNAKE_CASE__						)






  def 				__A (	self	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							int							,    SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							Union[str, Any]:
      # get log sigma
      __lowerCamelCase					      =			np.log(SCREAMING_SNAKE_CASE__						)

      # get distribution
      __lowerCamelCase					      =			log_sigma - log_sigmas[:, np.newaxis]

      # get sigmas range
      __lowerCamelCase					      =			np.cumsum((dists >= 0)							,    axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
      __lowerCamelCase					      =			low_idx + 1

      __lowerCamelCase					      =			log_sigmas[low_idx]
      __lowerCamelCase					      =			log_sigmas[high_idx]

      # interpolate sigmas
      __lowerCamelCase					      =			(low - log_sigma) / (low - high)
      __lowerCamelCase					      =			np.clip(SCREAMING_SNAKE_CASE__							,    0							,    1						)

      # transform interpolation to time range
      __lowerCamelCase					      =			(1 - w) * low_idx + w * high_idx
      __lowerCamelCase					      =			t.reshape(sigma.shape						)
      return t






  def 				__A (	self	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    SCREAMING_SNAKE_CASE__	:							Optional[int]						)  ->							torch.FloatTensor:
      __lowerCamelCase					      =			in_sigmas[-1].item()
      __lowerCamelCase					      =			in_sigmas[0].item()

      __lowerCamelCase					      =			7.0  # 7.0 is the value used in the paper
      __lowerCamelCase					      =			np.linspace(0							,    1							,    SCREAMING_SNAKE_CASE__						)
      __lowerCamelCase					      =			sigma_min ** (1 / rho)
      __lowerCamelCase					      =			sigma_max ** (1 / rho)
      __lowerCamelCase					      =			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas






  def 				__A (	self	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    SCREAMING_SNAKE_CASE__	:							Union[float, torch.FloatTensor]							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    SCREAMING_SNAKE_CASE__	:							float = 0.0							,    SCREAMING_SNAKE_CASE__	:							float = 0.0							,    SCREAMING_SNAKE_CASE__	:							float = float('''inf'''						)							,    SCREAMING_SNAKE_CASE__	:							float = 1.0							,    SCREAMING_SNAKE_CASE__	:							Optional[torch.Generator] = None							,    SCREAMING_SNAKE_CASE__	:							bool = True							,    )  ->							Union[EulerDiscreteSchedulerOutput, Tuple]:

      if (
          isinstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
          or isinstance(SCREAMING_SNAKE_CASE__							,    torch.IntTensor						)
          or isinstance(SCREAMING_SNAKE_CASE__							,    torch.LongTensor						)
      ):
          raise ValueError(
              (
                  '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                  ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
                  ''' one of the `scheduler.timesteps` as a timestep.'''
              )							,    )

      if not self.is_scale_input_called:
          logger.warning(
              '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
              '''See `StableDiffusionPipeline` for a usage example.'''						)

      if isinstance(SCREAMING_SNAKE_CASE__							,    torch.Tensor						):
          __lowerCamelCase					      =			timestep.to(self.timesteps.device						)

      __lowerCamelCase					      =			(self.timesteps == timestep).nonzero().item()
      __lowerCamelCase					      =			self.sigmas[step_index]

      __lowerCamelCase					      =			min(s_churn / (len(self.sigmas						) - 1)							,    2**0.5 - 1						) if s_tmin <= sigma <= s_tmax else 0.0

      __lowerCamelCase					      =			randn_tensor(
          model_output.shape							,    dtype=model_output.dtype							,    device=model_output.device							,    generator=SCREAMING_SNAKE_CASE__						)

      __lowerCamelCase					      =			noise * s_noise
      __lowerCamelCase					      =			sigma * (gamma + 1)

      if gamma > 0:
          __lowerCamelCase					      =			sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      # NOTE: "original_sample" should not be an expected prediction_type but is left in for
      # backwards compatibility
      if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
          __lowerCamelCase					      =			model_output
      elif self.config.prediction_type == "epsilon":
          __lowerCamelCase					      =			sample - sigma_hat * model_output
      elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          __lowerCamelCase					      =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      else:
          raise ValueError(
              f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

      # 2. Convert to an ODE derivative
      __lowerCamelCase					      =			(sample - pred_original_sample) / sigma_hat

      __lowerCamelCase					      =			self.sigmas[step_index + 1] - sigma_hat

      __lowerCamelCase					      =			sample + derivative * dt

      if not return_dict:
          return (prev_sample,)

      return EulerDiscreteSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__							,    pred_original_sample=SCREAMING_SNAKE_CASE__						)






  def 				__A (	self	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    SCREAMING_SNAKE_CASE__	:							torch.FloatTensor							,    )  ->							torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      __lowerCamelCase					      =			self.sigmas.to(device=original_samples.device							,    dtype=original_samples.dtype						)
      if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE__						):
          # mps does not support float64
          __lowerCamelCase					      =			self.timesteps.to(original_samples.device							,    dtype=torch.floataa						)
          __lowerCamelCase					      =			timesteps.to(original_samples.device							,    dtype=torch.floataa						)
      else:
          __lowerCamelCase					      =			self.timesteps.to(original_samples.device						)
          __lowerCamelCase					      =			timesteps.to(original_samples.device						)

      __lowerCamelCase					      =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

      __lowerCamelCase					      =			sigmas[step_indices].flatten()
      while len(sigma.shape						) < len(original_samples.shape						):
          __lowerCamelCase					      =			sigma.unsqueeze(-1						)

      __lowerCamelCase					      =			original_samples + noise * sigma
      return noisy_samples







  def __len__(	self	:							int						)  ->							List[str]:
      return self.config.num_train_timesteps





import inspect
import unittest

import requests

from transformers import MgpstrConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
					from PIL import Image

					from transformers import MgpstrProcessor

class      lowerCAmelCase__  :






		def __init__(	self	:							int							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]							,    SCREAMING_SNAKE_CASE__	:							List[Any]=False							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=13							,    SCREAMING_SNAKE_CASE__	:							Any=(32, 1_28)							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=4							,    SCREAMING_SNAKE_CASE__	:							List[Any]=3							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=27							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=38							,    SCREAMING_SNAKE_CASE__	:							Dict=99							,    SCREAMING_SNAKE_CASE__	:							int=99							,    SCREAMING_SNAKE_CASE__	:							Tuple=32							,    SCREAMING_SNAKE_CASE__	:							Any=5							,    SCREAMING_SNAKE_CASE__	:							str=4							,    SCREAMING_SNAKE_CASE__	:							List[str]=4.0							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=2_57							,    SCREAMING_SNAKE_CASE__	:							Tuple=None							,    )  ->							List[Any]:
						__lowerCamelCase					      =			parent
						__lowerCamelCase					      =			is_training
						__lowerCamelCase					      =			batch_size
						__lowerCamelCase					      =			image_size
						__lowerCamelCase					      =			patch_size
						__lowerCamelCase					      =			num_channels
						__lowerCamelCase					      =			max_token_length
						__lowerCamelCase					      =			num_character_labels
						__lowerCamelCase					      =			num_bpe_labels
						__lowerCamelCase					      =			num_wordpiece_labels
						__lowerCamelCase					      =			hidden_size
						__lowerCamelCase					      =			num_hidden_layers
						__lowerCamelCase					      =			num_attention_heads
						__lowerCamelCase					      =			mlp_ratio
						__lowerCamelCase					      =			patch_embeds_hidden_size
						__lowerCamelCase					      =			output_hidden_states






		def 				__A (	self	:							Any						)  ->							Optional[int]:
						__lowerCamelCase					      =			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)
						__lowerCamelCase					      =			self.get_config()
						return config, pixel_values






		def 				__A (	self	:							int						)  ->							Union[str, Any]:
						return MgpstrConfig(
						    image_size=self.image_size							,    patch_size=self.patch_size							,    num_channels=self.num_channels							,    max_token_length=self.max_token_length							,    num_character_labels=self.num_character_labels							,    num_bpe_labels=self.num_bpe_labels							,    num_wordpiece_labels=self.num_wordpiece_labels							,    hidden_size=self.hidden_size							,    num_hidden_layers=self.num_hidden_layers							,    num_attention_heads=self.num_attention_heads							,    mlp_ratio=self.mlp_ratio							,    output_hidden_states=self.output_hidden_states							,    )






		def 				__A (	self	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]						)  ->							List[Any]:
						__lowerCamelCase					      =			MgpstrForSceneTextRecognition(SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()
						with torch.no_grad():
										__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__						)
						self.parent.assertEqual(
						    generated_ids[0][0].shape							,    (self.batch_size, self.max_token_length, self.num_character_labels)						)







		def 				__A (	self	:							Dict						)  ->							Union[str, Any]:
						__lowerCamelCase					      =			self.prepare_config_and_inputs()
						__lowerCamelCase      ,					__lowerCamelCase					      =			config_and_inputs
						__lowerCamelCase					      =			{'''pixel_values''': pixel_values}
						return config, inputs_dict





@require_torch
class      lowerCAmelCase__  (			__lowercase						,			__lowercase						,			unittest.TestCase    ):
		a__ :		Dict   						=	(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
		a__ :		Optional[int]   						=	{"""feature-extraction""": MgpstrForSceneTextRecognition} if is_torch_available() else {}
		a__ :		Any   						=	False

		a__ :		List[Any]   						=	False
		a__ :		Any   						=	False
		a__ :		int   						=	False
		a__ :		Tuple   						=	False






		def 				__A (	self	:							Optional[Any]						)  ->							str:
						__lowerCamelCase					      =			MgpstrModelTester(self						)
						__lowerCamelCase					      =			ConfigTester(self							,    config_class=SCREAMING_SNAKE_CASE__							,    has_text_modality=SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							List[Any]						)  ->							Tuple:
						self.config_tester.run_common_tests()






		def 				__A (	self	:							List[str]						)  ->							Any:
						__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__						)






		@unittest.skip(reason='''MgpstrModel does not use inputs_embeds'''						)
		def 				__A (	self	:							Optional[int]						)  ->							Dict:
						pass






		def 				__A (	self	:							Tuple						)  ->							Tuple:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										self.assertIsInstance(model.get_input_embeddings()							,    (nn.Module)						)
										__lowerCamelCase					      =			model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__							,    nn.Linear						)						)






		def 				__A (	self	:							Tuple						)  ->							Optional[Any]:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										__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]							,    SCREAMING_SNAKE_CASE__						)






		@unittest.skip(reason='''MgpstrModel does not support feedforward chunking'''						)
		def 				__A (	self	:							Optional[Any]						)  ->							str:
						pass






		def 				__A (	self	:							str						)  ->							Tuple:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										if not model_class.supports_gradient_checkpointing:
														continue

										__lowerCamelCase					      =			True
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										self.assertTrue(model.is_gradient_checkpointing						)






		def 				__A (	self	:							List[str]						)  ->							List[str]:
						def check_hidden_states_output(SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]						):
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										model.to(SCREAMING_SNAKE_CASE__						)
										model.eval()

										with torch.no_grad():
														__lowerCamelCase					      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)						)

										__lowerCamelCase					      =			outputs.hidden_states

										__lowerCamelCase					      =			getattr(
										    self.model_tester							,    '''expected_num_hidden_layers'''							,    self.model_tester.num_hidden_layers + 1						)
										self.assertEqual(len(SCREAMING_SNAKE_CASE__						)							,    SCREAMING_SNAKE_CASE__						)

										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]						)							,    [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]							,    )

						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			True
										check_hidden_states_output(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										__lowerCamelCase					      =			True

										check_hidden_states_output(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							Tuple						)  ->							int:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						__lowerCamelCase					      =			_config_zero_init(SCREAMING_SNAKE_CASE__						)
						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			model_class(config=SCREAMING_SNAKE_CASE__						)
										for name, param in model.named_parameters():
														if isinstance(SCREAMING_SNAKE_CASE__							,    (nn.Linear, nn.Convad, nn.LayerNorm)						):
																		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'''							,    )







		@unittest.skip(reason='''Retain_grad is tested in individual model tests'''						)
		def 				__A (	self	:							int						)  ->							Union[str, Any]:
						pass





def 				__magic_name__				(     )    ->     Optional[Any]:
				__lowerCamelCase					      =			'''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
				__lowerCamelCase					      =			Image.open(requests.get(__lowerCAmelCase	,						stream=__lowerCAmelCase							).raw							).convert('''RGB'''							)
				return im







@require_vision
@require_torch
class      lowerCAmelCase__  (			unittest.TestCase    ):






		@slow
		def 				__A (	self	:							List[str]						)  ->							Optional[int]:
						__lowerCamelCase					      =			'''alibaba-damo/mgp-str-base'''
						__lowerCamelCase					      =			MgpstrForSceneTextRecognition.from_pretrained(SCREAMING_SNAKE_CASE__						).to(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			MgpstrProcessor.from_pretrained(SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			prepare_img()
						__lowerCamelCase					      =			processor(images=SCREAMING_SNAKE_CASE__							,    return_tensors='''pt'''						).pixel_values.to(SCREAMING_SNAKE_CASE__						)

						# forward pass
						with torch.no_grad():
										__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__						)

						# verify the logits
						self.assertEqual(outputs.logits[0].shape							,    torch.Size((1, 27, 38)						)						)

						__lowerCamelCase					      =			processor.batch_decode(outputs.logits						)
						__lowerCamelCase					      =			'''ticket'''

						self.assertEqual(out_strs['''generated_text'''][0]							,    SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			torch.tensor(
						    [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]							,    device=SCREAMING_SNAKE_CASE__							,    )

						self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]							,    SCREAMING_SNAKE_CASE__							,    atol=1e-4						)						)



