






"""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				,						_lowerCamelCase				,						_lowerCamelCase=False				,						_lowerCamelCase=1_3				,						_lowerCamelCase=(3_2, 1_2_8)				,						_lowerCamelCase=4				,						_lowerCamelCase=3				,						_lowerCamelCase=2_7				,						_lowerCamelCase=3_8				,						_lowerCamelCase=9_9				,						_lowerCamelCase=9_9				,						_lowerCamelCase=3_2				,						_lowerCamelCase=5				,						_lowerCamelCase=4				,						_lowerCamelCase=4.0				,						_lowerCamelCase=2_5_7				,						_lowerCamelCase=None				,						):
      lowercase				       =			parent
      lowercase				       =			is_training
      lowercase				       =			batch_size
      lowercase				       =			image_size
      lowercase				       =			patch_size
      lowercase				       =			num_channels
      lowercase				       =			max_token_length
      lowercase				       =			num_character_labels
      lowercase				       =			num_bpe_labels
      lowercase				       =			num_wordpiece_labels
      lowercase				       =			hidden_size
      lowercase				       =			num_hidden_layers
      lowercase				       =			num_attention_heads
      lowercase				       =			mlp_ratio
      lowercase				       =			patch_embeds_hidden_size
      lowercase				       =			output_hidden_states







    def      UpperCamelCase_ (     self			):
      lowercase				       =			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]			)
      lowercase				       =			self.get_config()
      return config, pixel_values







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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.prepare_config_and_inputs()
      lowercase       ,	lowercase				       =			config_and_inputs
      lowercase				       =			{'pixel_values': pixel_values}
      return config, inputs_dict


@require_torch
class 			a	(  a_,							a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	Optional[int]      	=(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
    UpperCAmelCase_					:	Optional[Any]      	={"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
    UpperCAmelCase_					:	Union[str, Any]      	=False

    UpperCAmelCase_					:	Optional[Any]      	=False
    UpperCAmelCase_					:	Dict      	=False
    UpperCAmelCase_					:	str      	=False
    UpperCAmelCase_					:	str      	=False







    def      UpperCamelCase_ (     self			):
      lowercase				       =			MgpstrModelTester(self			)
      lowercase				       =			ConfigTester(self				,						config_class=_lowerCamelCase				,						has_text_modality=_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      self.config_tester.run_common_tests()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			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      UpperCamelCase_ (     self			):
      pass







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

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







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

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

        lowercase				       =			['pixel_values']
        self.assertListEqual(arg_names[:1]				,						_lowerCamelCase			)







    @unittest.skip(reason='MgpstrModel does not support feedforward chunking'			)
    def      UpperCamelCase_ (     self			):
      pass







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			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

        lowercase				       =			True
        lowercase				       =			model_class(_lowerCamelCase			)
        self.assertTrue(model.is_gradient_checkpointing			)







    def      UpperCamelCase_ (     self			):
      def check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
        lowercase				       =			model_class(_lowerCamelCase			)
        model.to(_lowerCamelCase			)
        model.eval()

        with torch.no_grad():
          lowercase				       =			model(**self._prepare_for_class(_lowerCamelCase				,						_lowerCamelCase			)			)

        lowercase				       =			outputs.hidden_states

        lowercase				       =			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]				,						)

      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			True
        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)

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

        check_hidden_states_output(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase       ,	lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      lowercase				       =			_config_zero_init(_lowerCamelCase			)
      for model_class in self.all_model_classes:
        lowercase				       =			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      UpperCamelCase_ (     self			):
      pass


def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			'https://i.postimg.cc/ZKwLg2Gw/367-14.png'
  lowercase				       =			Image.open(requests.get(__snake_case						,   stream=__snake_case       ).raw       ).convert('RGB'       )
  return im



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







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			'alibaba-damo/mgp-str-base'
      lowercase				       =			MgpstrForSceneTextRecognition.from_pretrained(_lowerCamelCase			).to(_lowerCamelCase			)
      lowercase				       =			MgpstrProcessor.from_pretrained(_lowerCamelCase			)

      lowercase				       =			prepare_img()
      lowercase				       =			processor(images=_lowerCamelCase				,						return_tensors='pt'			).pixel_values.to(_lowerCamelCase			)

      # forward pass
      with torch.no_grad():
        lowercase				       =			model(_lowerCamelCase			)

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

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

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

      lowercase				       =			torch.tensor(
          [[[-3_9.5_3_9_7, -4_4.4_0_2_4, -3_6.1_8_4_4], [-6_1.4_7_0_9, -6_3.8_6_3_9, -5_8.3_4_5_4], [-7_4.0_2_2_5, -6_8.5_4_9_4, -7_1.2_1_6_4]]]				,						device=_lowerCamelCase				,						)

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






"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class   __A  (snake_case__    ,		snake_case__    ,		snake_case__):





							'''simple docstring'''

							@register_to_config
							def __init__(					self				:   List[str]		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   float		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   bool = False		,							)       ->Dict:



														"""simple docstring"""



														super().__init__()

														snake_case_     							=		nn.Linear(UpperCAmelCase_		,							UpperCAmelCase_		,							bias=UpperCAmelCase_							)

														snake_case_     							=		nn.Embedding(UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		False

														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)

														snake_case_     							=		TaConfig(
														    d_model=UpperCAmelCase_		,							num_heads=UpperCAmelCase_		,							d_kv=UpperCAmelCase_		,							d_ff=UpperCAmelCase_		,							feed_forward_proj=UpperCAmelCase_		,							dropout_rate=UpperCAmelCase_		,							is_decoder=UpperCAmelCase_		,							is_encoder_decoder=UpperCAmelCase_		,							)
														snake_case_     							=		nn.ModuleList()
														for lyr_num in range(UpperCAmelCase_							):
																					snake_case_     							=		TaBlock(UpperCAmelCase_							)
																					self.encoders.append(UpperCAmelCase_							)

														snake_case_     							=		TaLayerNorm(UpperCAmelCase_							)
														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   List[str]							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		self.input_proj(UpperCAmelCase_							)

														# terminal relative positional encodings
														snake_case_     							=		encoder_inputs.shape[1]
														snake_case_     							=		torch.arange(UpperCAmelCase_		,							device=encoder_inputs.device							)

														snake_case_     							=		encoder_inputs_mask.sum(-1							)
														snake_case_     							=		torch.roll(input_positions.unsqueeze(0							)		,							tuple(seq_lens.tolist()							)		,							dims=0							)
														x += self.position_encoding(UpperCAmelCase_							)

														snake_case_     							=		self.dropout_pre(UpperCAmelCase_							)

														# inverted the attention mask
														snake_case_     							=		encoder_inputs.size()
														snake_case_     							=		self.get_extended_attention_mask(UpperCAmelCase_		,							UpperCAmelCase_							)

														for lyr in self.encoders:
																					snake_case_     							=		lyr(UpperCAmelCase_		,							UpperCAmelCase_							)[0]
														snake_case_     							=		self.layer_norm(UpperCAmelCase_							)

														return self.dropout_post(UpperCAmelCase_							), encoder_inputs_mask



