
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        UpperCamelCase__:

	"""simple docstring"""

	def __init__(      self				:			Dict							,			snake_case__				:			Any							,			snake_case__				:			List[str]=False							,			snake_case__				:			Union[str, Any]=13							,			snake_case__				:			Union[str, Any]=(32, 1_28)							,			snake_case__				:			int=4							,			snake_case__				:			Union[str, Any]=3							,			snake_case__				:			Any=27							,			snake_case__				:			str=38							,			snake_case__				:			Dict=99							,			snake_case__				:			Union[str, Any]=99							,			snake_case__				:			Dict=32							,			snake_case__				:			Tuple=5							,			snake_case__				:			List[Any]=4							,			snake_case__				:			Any=4.0							,			snake_case__				:			Optional[int]=2_57							,			snake_case__				:			Tuple=None							,			):
			"""simple docstring"""





			A				=parent
			A				=is_training
			A				=batch_size
			A				=image_size
			A				=patch_size
			A				=num_channels
			A				=max_token_length
			A				=num_character_labels
			A				=num_bpe_labels
			A				=num_wordpiece_labels
			A				=hidden_size
			A				=num_hidden_layers
			A				=num_attention_heads
			A				=mlp_ratio
			A				=patch_embeds_hidden_size
			A				=output_hidden_states







	def        _a					(      self				:			str ):
			"""simple docstring"""





			A				=floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
			A				=self.get_config()
			return config, pixel_values







	def        _a					(      self				:			List[Any] ):
			"""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        _a					(      self				:			List[Any]							,			snake_case__				:			Union[str, Any]							,			snake_case__				:			str ):
			"""simple docstring"""





			A				=MgpstrForSceneTextRecognition(snake_case__ )
			model.to(snake_case__ )
			model.eval()
			with torch.no_grad():
					A				=model(snake_case__ )
			self.parent.assertEqual(
			    generated_ids[0][0].shape							,			(self.batch_size, self.max_token_length, self.num_character_labels) )







	def        _a					(      self				:			int ):
			"""simple docstring"""





			A				=self.prepare_config_and_inputs()
			A			,    A				=config_and_inputs
			A				={"pixel_values": pixel_values}
			return config, inputs_dict


@require_torch
class        UpperCamelCase__(    lowerCAmelCase__						,						lowerCAmelCase__						,						unittest.TestCase	):

	"""simple docstring"""

	_A  =		(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
	_A  =		{"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
	_A  =		False

	_A  =		False
	_A  =		False
	_A  =		False
	_A  =		False







	def        _a					(      self				:			Union[str, Any] ):
			"""simple docstring"""





			A				=MgpstrModelTester(self )
			A				=ConfigTester(self							,			config_class=snake_case__							,			has_text_modality=snake_case__ )







	def        _a					(      self				:			int ):
			"""simple docstring"""





			self.config_tester.run_common_tests()







	def        _a					(      self				:			Union[str, Any] ):
			"""simple docstring"""





			A				=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*snake_case__ )







	@unittest.skip(reason="MgpstrModel does not use inputs_embeds" )
	def        _a					(      self				:			List[str] ):
			"""simple docstring"""





			pass







	def        _a					(      self				:			Union[str, Any] ):
			"""simple docstring"""





			A			,    A				=self.model_tester.prepare_config_and_inputs_for_common()

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







	def        _a					(      self				:			Any ):
			"""simple docstring"""





			A			,    A				=self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					A				=model_class(snake_case__ )
					A				=inspect.signature(model.forward )
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					A				=[*signature.parameters.keys()]

					A				=["pixel_values"]
					self.assertListEqual(arg_names[:1]							,			snake_case__ )







	@unittest.skip(reason="MgpstrModel does not support feedforward chunking" )
	def        _a					(      self				:			Tuple ):
			"""simple docstring"""





			pass







	def        _a					(      self				:			Dict ):
			"""simple docstring"""





			A			,    A				=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

					A				=True
					A				=model_class(snake_case__ )
					self.assertTrue(model.is_gradient_checkpointing )







	def        _a					(      self				:			Union[str, Any] ):
			"""simple docstring"""





			def check_hidden_states_output(snake_case__				:			Tuple							,			snake_case__				:			Optional[int]							,			snake_case__				:			Optional[Any] ):
					A				=model_class(snake_case__ )
					model.to(snake_case__ )
					model.eval()

					with torch.no_grad():
							A				=model(**self._prepare_for_class(snake_case__							,			snake_case__ ) )

					A				=outputs.hidden_states

					A				=getattr(
					    self.model_tester							,			"expected_num_hidden_layers"							,			self.model_tester.num_hidden_layers + 1 )
					self.assertEqual(len(snake_case__ )							,			snake_case__ )

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

			A			,    A				=self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					A				=True
					check_hidden_states_output(snake_case__							,			snake_case__							,			snake_case__ )

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

					check_hidden_states_output(snake_case__							,			snake_case__							,			snake_case__ )







	def        _a					(      self				:			str ):
			"""simple docstring"""





			A			,    A				=self.model_tester.prepare_config_and_inputs_for_common()

			A				=_config_zero_init(snake_case__ )
			for model_class in self.all_model_classes:
					A				=model_class(config=snake_case__ )
					for name, param in model.named_parameters():
							if isinstance(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 ):
			"""simple docstring"""





			pass


def    UpperCamelCase_ (	)     ->Tuple:
		A				="https://i.postimg.cc/ZKwLg2Gw/367-14.png"
		A				=Image.open(requests.get(a_  ,				stream=a_		).raw		).convert("RGB"		)
		return im






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

	"""simple docstring"""

	@slow
	def        _a					(      self				:			Tuple ):
			"""simple docstring"""





			A				="alibaba-damo/mgp-str-base"
			A				=MgpstrForSceneTextRecognition.from_pretrained(snake_case__ ).to(snake_case__ )
			A				=MgpstrProcessor.from_pretrained(snake_case__ )

			A				=prepare_img()
			A				=processor(images=snake_case__							,			return_tensors="pt" ).pixel_values.to(snake_case__ )

			# forward pass
			with torch.no_grad():
					A				=model(snake_case__ )

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

			A				=processor.batch_decode(outputs.logits )
			A				="ticket"

			self.assertEqual(out_strs["generated_text"][0]							,			snake_case__ )

			A				=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=snake_case__							,			)

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






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


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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		42
	_A  =		None


def    UpperCamelCase_ (	a_  ,				a_=0.999  ,				a_="cosine"  ,				)     ->List[Any]:
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(a_		):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

		elif alpha_transform_type == "exp":

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

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

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






class        UpperCamelCase__(    lowerCAmelCase__						,						lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		[e.name for e in KarrasDiffusionSchedulers]
	_A  =		1







	@register_to_config
	def __init__(      self				:			Any							,			snake_case__				:			int = 10_00							,			snake_case__				:			float = 0.0_001							,			snake_case__				:			float = 0.02							,			snake_case__				:			str = "linear"							,			snake_case__				:			Optional[Union[np.ndarray, List[float]]] = None							,			snake_case__				:			str = "epsilon"							,			snake_case__				:			str = "linear"							,			snake_case__				:			Optional[bool] = False							,			snake_case__				:			str = "linspace"							,			snake_case__				:			int = 0							,			):
			"""simple docstring"""





			if trained_betas is not None:
					A				=torch.tensor(snake_case__							,			dtype=torch.floataa )
			elif beta_schedule == "linear":
					A				=torch.linspace(snake_case__							,			snake_case__							,			snake_case__							,			dtype=torch.floataa )
			elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					A				=(
					    torch.linspace(beta_start**0.5							,			beta_end**0.5							,			snake_case__							,			dtype=torch.floataa ) ** 2
					)
			elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					A				=betas_for_alpha_bar(snake_case__ )
			else:
					raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

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

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

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







	@property
	def        _a					(      self				:			Dict ):
			"""simple docstring"""





			if self.config.timestep_spacing in ["linspace", "trailing"]:
					return self.sigmas.max()

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







	def        _a					(      self				:			str							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			Union[float, torch.FloatTensor] ):
			"""simple docstring"""





			if isinstance(snake_case__							,			torch.Tensor ):
					A				=timestep.to(self.timesteps.device )
			A				=(self.timesteps == timestep).nonzero().item()
			A				=self.sigmas[step_index]

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

			A				=True
			return sample







	def        _a					(      self				:			str							,			snake_case__				:			int							,			snake_case__				:			Union[str, torch.device] = None ):
			"""simple docstring"""





			A				=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":
					A				=np.linspace(0							,			self.config.num_train_timesteps - 1							,			snake_case__							,			dtype=snake_case__ )[
					    ::-1
					].copy()
			elif self.config.timestep_spacing == "leading":
					A				=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
					A				=(np.arange(0							,			snake_case__ ) * step_ratio).round()[::-1].copy().astype(snake_case__ )
					timesteps += self.config.steps_offset
			elif self.config.timestep_spacing == "trailing":
					A				=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
					A				=(np.arange(self.config.num_train_timesteps							,			0							,			-step_ratio )).round().copy().astype(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\'.''' )

			A				=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
			A				=np.log(snake_case__ )

			if self.config.interpolation_type == "linear":
					A				=np.interp(snake_case__							,			np.arange(0							,			len(snake_case__ ) )							,			snake_case__ )
			elif self.config.interpolation_type == "log_linear":
					A				=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:
					A				=self._convert_to_karras(in_sigmas=snake_case__							,			num_inference_steps=self.num_inference_steps )
					A				=np.array([self._sigma_to_t(snake_case__							,			snake_case__ ) for sigma in sigmas] )

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







	def        _a					(      self				:			Dict							,			snake_case__				:			Dict							,			snake_case__				:			Any ):
			"""simple docstring"""





			A				=np.log(snake_case__ )

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

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

			A				=log_sigmas[low_idx]
			A				=log_sigmas[high_idx]

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

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







	def        _a					(      self				:			List[str]							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			Dict ):
			"""simple docstring"""





			A				=in_sigmas[-1].item()
			A				=in_sigmas[0].item()

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







	def        _a					(      self				:			Any							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			Union[float, torch.FloatTensor]							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			float = 0.0							,			snake_case__				:			float = 0.0							,			snake_case__				:			float = float("inf" )							,			snake_case__				:			float = 1.0							,			snake_case__				:			Optional[torch.Generator] = None							,			snake_case__				:			bool = True							,			):
			"""simple docstring"""





			if (
			    isinstance(snake_case__							,			snake_case__ )
			    or isinstance(snake_case__							,			torch.IntTensor )
			    or isinstance(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(snake_case__							,			torch.Tensor ):
					A				=timestep.to(self.timesteps.device )

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

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

			A				=randn_tensor(
			    model_output.shape							,			dtype=model_output.dtype							,			device=model_output.device							,			generator=snake_case__ )

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

			if gamma > 0:
					A				=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":
					A				=model_output
			elif self.config.prediction_type == "epsilon":
					A				=sample - sigma_hat * model_output
			elif self.config.prediction_type == "v_prediction":
					# * c_out + input * c_skip
					A				=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				=(sample - pred_original_sample) / sigma_hat

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

			A				=sample + derivative * dt

			if not return_dict:
					return (prev_sample,)

			return EulerDiscreteSchedulerOutput(prev_sample=snake_case__							,			pred_original_sample=snake_case__ )







	def        _a					(      self				:			Optional[int]							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			torch.FloatTensor							,			):
			"""simple docstring"""





			A				=self.sigmas.to(device=original_samples.device							,			dtype=original_samples.dtype )
			if original_samples.device.type == "mps" and torch.is_floating_point(snake_case__ ):
					# mps does not support float64
					A				=self.timesteps.to(original_samples.device							,			dtype=torch.floataa )
					A				=timesteps.to(original_samples.device							,			dtype=torch.floataa )
			else:
					A				=self.timesteps.to(original_samples.device )
					A				=timesteps.to(original_samples.device )

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

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

			A				=original_samples + noise * sigma
			return noisy_samples







	def __len__(      self				:			Tuple ):
			"""simple docstring"""





			return self.config.num_train_timesteps




