
'''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 			UpperCamelCase__	:






 def __init__(      self	:Union[str, Any]						,					_A	:List[Any]						,					_A	:Tuple=False						,					_A	:int=13						,					_A	:Any=(32, 128)						,					_A	:Any=4						,					_A	:Union[str, Any]=3						,					_A	:List[str]=27						,					_A	:Union[str, Any]=38						,					_A	:Optional[Any]=99						,					_A	:Tuple=99						,					_A	:Tuple=32						,					_A	:str=5						,					_A	:int=4						,					_A	:List[Any]=4.0						,					_A	:List[Any]=257						,					_A	:Any=None						,					)					->      str:







       '''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 						lowercase_    (      self	:List[str]      )					->      Tuple:







       '''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 						lowercase_    (      self	:Optional[int]      )					->      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 						lowercase_    (      self	:List[str]						,					_A	:List[Any]						,					_A	:Any      )					->      List[str]:







       '''simple docstring'''



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






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







       '''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):
 UpperCAmelCase__     :     Optional[int]    					=       (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
 UpperCAmelCase__     :     List[Any]    					=       {'feature-extraction': MgpstrForSceneTextRecognition} if is_torch_available() else {}
 UpperCAmelCase__     :     str    					=       False

 UpperCAmelCase__     :     str    					=       False
 UpperCAmelCase__     :     Dict    					=       False
 UpperCAmelCase__     :     Union[str, Any]    					=       False
 UpperCAmelCase__     :     int    					=       False






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







       '''simple docstring'''



       __A													=      MgpstrModelTester(self      )
       __A													=      ConfigTester(self						,					config_class=lowerCAmelCase_						,					has_text_modality=lowerCAmelCase_      )






 def 						lowercase_    (      self	:List[Any]      )					->      List[str]:







       '''simple docstring'''



       self.config_tester.run_common_tests()






 def 						lowercase_    (      self	:int      )					->      str:







       '''simple docstring'''



       __A													=      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 						lowercase_    (      self	:Tuple      )					->      str:







       '''simple docstring'''



       pass






 def 						lowercase_    (      self	:Optional[int]      )					->      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(lowerCAmelCase_      )
             self.assertIsInstance(model.get_input_embeddings()						,					(nn.Module)      )
             __A													=      model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(lowerCAmelCase_						,					nn.Linear      )      )






 def 						lowercase_    (      self	:Dict      )					->      Dict:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __A													=      model_class(lowerCAmelCase_      )
             __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]						,					lowerCAmelCase_      )






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







       '''simple docstring'''



       pass






 def 						lowercase_    (      self	:Dict      )					->      int:







       '''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(lowerCAmelCase_      )
             self.assertTrue(model.is_gradient_checkpointing      )






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







       '''simple docstring'''



       def check_hidden_states_output(_A	:List[str]						,					_A	:Dict						,					_A	:Dict      ):
             __A													=      model_class(lowerCAmelCase_      )
             model.to(lowerCAmelCase_      )
             model.eval()

             with torch.no_grad():
                   __A													=      model(**self._prepare_for_class(lowerCAmelCase_						,					lowerCAmelCase_      )      )

             __A													=      outputs.hidden_states

             __A													=      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]						,					)

       __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(lowerCAmelCase_						,					lowerCAmelCase_						,					lowerCAmelCase_      )

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

             check_hidden_states_output(lowerCAmelCase_						,					lowerCAmelCase_						,					lowerCAmelCase_      )






 def 						lowercase_    (      self	:Tuple      )					->      Optional[int]:







       '''simple docstring'''



       __A						,   __A													=      self.model_tester.prepare_config_and_inputs_for_common()

       __A													=      _config_zero_init(lowerCAmelCase_      )
       for model_class in self.all_model_classes:
             __A													=      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 						lowercase_    (      self	:Tuple      )					->      List[str]:







       '''simple docstring'''



       pass





def 							snake_case				(		)->      Tuple:







      """simple docstring"""
      __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):






 @slow
 def 						lowercase_    (      self	:Optional[int]      )					->      str:







       '''simple docstring'''



       __A													=      'alibaba-damo/mgp-str-base'
       __A													=      MgpstrForSceneTextRecognition.from_pretrained(lowerCAmelCase_      ).to(lowerCAmelCase_      )
       __A													=      MgpstrProcessor.from_pretrained(lowerCAmelCase_      )

       __A													=      prepare_img()
       __A													=      processor(images=lowerCAmelCase_						,					return_tensors='pt'      ).pixel_values.to(lowerCAmelCase_      )

       # forward pass
       with torch.no_grad():
             __A													=      model(lowerCAmelCase_      )

       # 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]						,					lowerCAmelCase_      )

       __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=lowerCAmelCase_						,					)

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







from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCAmelCase__  	=      logging.get_logger(__name__)

lowerCAmelCase__  	=      {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'longt5'
	__SCREAMING_SNAKE_CASE              =					['past_key_values']
	__SCREAMING_SNAKE_CASE              =					{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


	def __init__(   self       ,  lowerCAmelCase_=32128       ,  lowerCAmelCase_=512       ,  lowerCAmelCase_=64       ,  lowerCAmelCase_=2048       ,  lowerCAmelCase_=6       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=8       ,  lowerCAmelCase_=127       ,  lowerCAmelCase_=16       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=128       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=1e-6       ,  lowerCAmelCase_=1.0       ,  lowerCAmelCase_="relu"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_="local"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=0       ,  lowerCAmelCase_=1       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =vocab_size
		_lowercase   =d_model
		_lowercase   =d_kv
		_lowercase   =d_ff
		_lowercase   =num_layers
		# default = symmetry
		_lowercase   =num_decoder_layers if num_decoder_layers is not None else self.num_layers
		_lowercase   =num_heads
		_lowercase   =local_radius
		_lowercase   =global_block_size
		_lowercase   =relative_attention_num_buckets
		_lowercase   =relative_attention_max_distance
		_lowercase   =dropout_rate
		_lowercase   =layer_norm_epsilon
		_lowercase   =initializer_factor
		_lowercase   =feed_forward_proj
		_lowercase   =encoder_attention_type
		_lowercase   =use_cache

		_lowercase   =self.feed_forward_proj.split("-"       )
		_lowercase   =act_info[-1]
		_lowercase   =act_info[0] == "gated"

		if len(lowerCAmelCase_       ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_       ) > 2:
			raise ValueError(
			    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
			    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
			    "'gated-gelu' or 'relu'"       )

		# for backwards compatibility
		if feed_forward_proj == "gated-gelu":
			_lowercase   ="gelu_new"

		super().__init__(
		    pad_token_id=lowerCAmelCase_       ,  eos_token_id=lowerCAmelCase_       ,  is_encoder_decoder=lowerCAmelCase_       ,  **lowerCAmelCase_       ,  )

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	@property
	def __lowerCAmelCase      (   self       ):
		_lowercase   ={
		    "input_ids": {0: "batch", 1: "encoder_sequence"},
		    "attention_mask": {0: "batch", 1: "encoder_sequence"},
		}
		if self.use_past:
			_lowercase   ="past_encoder_sequence + sequence"
			_lowercase   ={0: "batch"}
			_lowercase   ={0: "batch", 1: "past_decoder_sequence + sequence"}
		else:
			_lowercase   ={0: "batch", 1: "decoder_sequence"}
			_lowercase   ={0: "batch", 1: "decoder_sequence"}

		if self.use_past:
			self.fill_with_past_key_values_(lowerCAmelCase_       ,  direction="inputs"       )

		return common_inputs


	@property
	def __lowerCAmelCase      (   self       ):
		return 13




