





"""simple docstring"""




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 					_a		:






				def __init__(					self						:       str,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       List[str]=False,					lowerCAmelCase__						:       int=1_3,					lowerCAmelCase__						:       str=(3_2, 1_2_8),					lowerCAmelCase__						:       List[Any]=4,					lowerCAmelCase__						:       Any=3,					lowerCAmelCase__						:       Union[str, Any]=2_7,					lowerCAmelCase__						:       List[Any]=3_8,					lowerCAmelCase__						:       Union[str, Any]=9_9,					lowerCAmelCase__						:       Optional[Any]=9_9,					lowerCAmelCase__						:       Any=3_2,					lowerCAmelCase__						:       Dict=5,					lowerCAmelCase__						:       Optional[int]=4,					lowerCAmelCase__						:       Optional[int]=4.0,					lowerCAmelCase__						:       int=2_5_7,					lowerCAmelCase__						:       Any=None,					)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       parent
										_UpperCamelCase     :   str      =       is_training
										_UpperCamelCase     :   Union[str, Any]      =       batch_size
										_UpperCamelCase     :   int      =       image_size
										_UpperCamelCase     :   List[str]      =       patch_size
										_UpperCamelCase     :   List[str]      =       num_channels
										_UpperCamelCase     :   str      =       max_token_length
										_UpperCamelCase     :   Union[str, Any]      =       num_character_labels
										_UpperCamelCase     :   int      =       num_bpe_labels
										_UpperCamelCase     :   Optional[int]      =       num_wordpiece_labels
										_UpperCamelCase     :   Optional[Any]      =       hidden_size
										_UpperCamelCase     :   List[str]      =       num_hidden_layers
										_UpperCamelCase     :   int      =       num_attention_heads
										_UpperCamelCase     :   str      =       mlp_ratio
										_UpperCamelCase     :   Any      =       patch_embeds_hidden_size
										_UpperCamelCase     :   Optional[int]      =       output_hidden_states






				def 							snake_case      (					self						:       List[Any]				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]				)
										_UpperCamelCase     :   Union[str, Any]      =       self.get_config()
										return config, pixel_values






				def 							snake_case      (					self						:       str				)      -> Optional[int]:




										'''simple docstring'''



										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 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Optional[Any]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       MgpstrForSceneTextRecognition(lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										with torch.no_grad():
																_UpperCamelCase     :   Union[str, Any]      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(
										    generated_ids[0][0].shape,					(self.batch_size, self.max_token_length, self.num_character_labels)				)






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




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.prepare_config_and_inputs()
										_UpperCamelCase  ,				_UpperCamelCase     :   Union[str, Any]      =       config_and_inputs
										_UpperCamelCase     :   Optional[Any]      =       {'''pixel_values''': pixel_values}
										return config, inputs_dict




@require_torch
class 					_a		( _lowerCAmelCase      ,							_lowerCAmelCase      ,							unittest.TestCase							):
				UpperCamelCase								=       (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
				UpperCamelCase								=       {'''feature-extraction''': MgpstrForSceneTextRecognition} if is_torch_available() else {}
				UpperCamelCase								=       False

				UpperCamelCase								=       False
				UpperCamelCase								=       False
				UpperCamelCase								=       False
				UpperCamelCase								=       False






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




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       MgpstrModelTester(self				)
										_UpperCamelCase     :   Union[str, Any]      =       ConfigTester(self,					config_class=lowerCAmelCase__,					has_text_modality=lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any				)      -> Tuple:




										'''simple docstring'''



										self.config_tester.run_common_tests()






				def 							snake_case      (					self						:       str				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__				)






				@unittest.skip(reason='''MgpstrModel does not use inputs_embeds'''				)
				def 							snake_case      (					self						:       int				)      -> List[Any]:




										'''simple docstring'''



										pass






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




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   Union[str, Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase     :   Optional[int]      =       model_class(lowerCAmelCase__				)
																self.assertIsInstance(model.get_input_embeddings(),					(nn.Module)				)
																_UpperCamelCase     :   Optional[int]      =       model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(lowerCAmelCase__,					nn.Linear				)				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   List[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase     :   List[Any]      =       model_class(lowerCAmelCase__				)
																_UpperCamelCase     :   str      =       inspect.signature(model.forward				)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_UpperCamelCase     :   Dict      =       [*signature.parameters.keys()]

																_UpperCamelCase     :   List[Any]      =       ['''pixel_values''']
																self.assertListEqual(arg_names[:1],					lowerCAmelCase__				)






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




										'''simple docstring'''



										pass






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




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   Union[str, Any]      =       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

																_UpperCamelCase     :   Optional[Any]      =       True
																_UpperCamelCase     :   Union[str, Any]      =       model_class(lowerCAmelCase__				)
																self.assertTrue(model.is_gradient_checkpointing				)






				def 							snake_case      (					self						:       Any				)      -> List[Any]:




										'''simple docstring'''



										def check_hidden_states_output(lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       List[str]				):
																_UpperCamelCase     :   Optional[int]      =       model_class(lowerCAmelCase__				)
																model.to(lowerCAmelCase__				)
																model.eval()

																with torch.no_grad():
																						_UpperCamelCase     :   Optional[int]      =       model(**self._prepare_for_class(lowerCAmelCase__,					lowerCAmelCase__				)				)

																_UpperCamelCase     :   str      =       outputs.hidden_states

																_UpperCamelCase     :   Tuple      =       getattr(
																    self.model_tester,					'''expected_num_hidden_layers''',					self.model_tester.num_hidden_layers + 1				)
																self.assertEqual(len(lowerCAmelCase__				),					lowerCAmelCase__				)

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

										_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase     :   Any      =       True
																check_hidden_states_output(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																_UpperCamelCase     :   Dict      =       True

																check_hidden_states_output(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   Optional[int]      =       self.model_tester.prepare_config_and_inputs_for_common()

										_UpperCamelCase     :   Union[str, Any]      =       _config_zero_init(lowerCAmelCase__				)
										for model_class in self.all_model_classes:
																_UpperCamelCase     :   Union[str, Any]      =       model_class(config=lowerCAmelCase__				)
																for name, param in model.named_parameters():
																						if isinstance(lowerCAmelCase__,					(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 							snake_case      (					self						:       int				)      -> Tuple:




										'''simple docstring'''



										pass





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






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






				@slow
				def 							snake_case      (					self						:       List[str]				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       '''alibaba-damo/mgp-str-base'''
										_UpperCamelCase     :   Optional[int]      =       MgpstrForSceneTextRecognition.from_pretrained(lowerCAmelCase__				).to(lowerCAmelCase__				)
										_UpperCamelCase     :   Union[str, Any]      =       MgpstrProcessor.from_pretrained(lowerCAmelCase__				)

										_UpperCamelCase     :   Any      =       prepare_img()
										_UpperCamelCase     :   List[str]      =       processor(images=lowerCAmelCase__,					return_tensors='''pt'''				).pixel_values.to(lowerCAmelCase__				)

										# forward pass
										with torch.no_grad():
																_UpperCamelCase     :   List[str]      =       model(lowerCAmelCase__				)

										# verify the logits
										self.assertEqual(outputs.logits[0].shape,					torch.Size((1, 2_7, 3_8)				)				)

										_UpperCamelCase     :   List[Any]      =       processor.batch_decode(outputs.logits				)
										_UpperCamelCase     :   List[str]      =       '''ticket'''

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

										_UpperCamelCase     :   int      =       torch.tensor(
										    [[[-39.5_397, -44.4_024, -36.1_844], [-61.4_709, -63.8_639, -58.3_454], [-74.0_225, -68.5_494, -71.2_164]]],					device=lowerCAmelCase__,					)

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







"""simple docstring"""






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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)


@flax.struct.dataclass
class 	A_       :





  """simple docstring"""

  lowercase		:	CommonSchedulerState

  # setable values
  lowercase		:	jnp.ndarray
  lowercase		:	jnp.ndarray
  lowercase		:	jnp.ndarray
  lowercase		:	Optional[int]   =						None

  # running values
  lowercase		:	Optional[jnp.ndarray]   =						None
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     return cls(common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase						)



@dataclass
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	LMSDiscreteSchedulerState



class 	A_       (     _UpperCAmelCase      ,  _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	int   =						[e.name for e in FlaxKarrasDiffusionSchedulers]

  lowercase		:	jnp.dtype
  @property
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     return True
  @register_to_config
  def __init__(						self  ,					__UpperCAmelCase = 10_00  ,					__UpperCAmelCase = 0.0001  ,					__UpperCAmelCase = 0.02  ,					__UpperCAmelCase = "linear"  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "epsilon"  ,					__UpperCAmelCase = jnp.floataa  ,					)				->					Union[str, Any]:
     a   :	Dict						=    dtype
  def 							lowercase_     (						self  ,					__UpperCAmelCase = None						)				->					LMSDiscreteSchedulerState:
     if common is None:
        a   :	Dict						=    CommonSchedulerState.create(self						)

     a   :	List[Any]						=    jnp.arange(0  ,					self.config.num_train_timesteps						).round()[::-1]
     a   :	Dict						=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

     # standard deviation of the initial noise distribution
     a   :	Union[str, Any]						=    sigmas.max()

     return LMSDiscreteSchedulerState.create(
         common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					jnp.ndarray:
     ((a)	, )   :	Optional[int]						=    jnp.where(state.timesteps == timestep  ,					size=1						)
     a   :	Optional[Any]						=    step_index[0]

     a   :	Union[str, Any]						=    state.sigmas[step_index]
     a   :	Optional[int]						=    sample / ((sigma**2 + 1) ** 0.5)
     return sample
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:

     def lms_derivative(__UpperCAmelCase						):
        a   :	Optional[Any]						=    1.0
        for k in range(__UpperCAmelCase						):
           if current_order == k:
              continue
           prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
        return prod

     a   :	Union[str, Any]						=    integrate.quad(__UpperCAmelCase  ,					state.sigmas[t]  ,					state.sigmas[t + 1]  ,					epsrel=1E-4						)[0]

     return integrated_coeff
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = ()						)				->					LMSDiscreteSchedulerState:
     a   :	Optional[int]						=    jnp.linspace(self.config.num_train_timesteps - 1  ,					0  ,					__UpperCAmelCase  ,					dtype=self.dtype						)

     a   :	Dict						=    jnp.floor(__UpperCAmelCase						).astype(jnp.intaa						)
     a   :	str						=    jnp.ceil(__UpperCAmelCase						).astype(jnp.intaa						)

     a   :	List[Any]						=    jnp.mod(__UpperCAmelCase  ,					1.0						)

     a   :	Optional[int]						=    ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
     a   :	int						=    (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
     a   :	Optional[int]						=    jnp.concatenate([sigmas, jnp.array([0.0]  ,					dtype=self.dtype						)]						)

     a   :	Any						=    timesteps.astype(jnp.intaa						)

     # initial running values
     a   :	Optional[int]						=    jnp.zeros((0,) + shape  ,					dtype=self.dtype						)

     return state.replace(
         timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					num_inference_steps=__UpperCAmelCase  ,					derivatives=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = 4  ,					__UpperCAmelCase = True  ,					)				->					Union[FlaxLMSSchedulerOutput, Tuple]:
     if state.num_inference_steps is None:
        raise ValueError(
            'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

     a   :	Dict						=    state.sigmas[timestep]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        a   :	Any						=    sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        a   :	str						=    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
     a   :	str						=    (sample - pred_original_sample) / sigma
     a   :	int						=    state.replace(derivatives=jnp.append(state.derivatives  ,					__UpperCAmelCase						)						)
     if len(state.derivatives						) > order:
        a   :	Any						=    state.replace(derivatives=jnp.delete(state.derivatives  ,					0						)						)

     # 3. Compute linear multistep coefficients
     a   :	Any						=    min(timestep + 1  ,					__UpperCAmelCase						)
     a   :	Dict						=    [self.get_lms_coefficient(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						) for curr_order in range(__UpperCAmelCase						)]

     # 4. Compute previous sample based on the derivatives path
     a   :	int						=    sample + sum(
         coeff * derivative for coeff, derivative in zip(__UpperCAmelCase  ,					reversed(state.derivatives						)						)						)

     if not return_dict:
        return (prev_sample, state)

     return FlaxLMSSchedulerOutput(prev_sample=__UpperCAmelCase  ,					state=__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					jnp.ndarray:
     a   :	Dict						=    state.sigmas[timesteps].flatten()
     a   :	Union[str, Any]						=    broadcast_to_shape_from_left(__UpperCAmelCase  ,					noise.shape						)

     a   :	List[Any]						=    original_samples + noise * sigma

     return noisy_samples






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





