
import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
     from tokenizers import Tokenizer
     from tokenizers.models import WordPiece

class      lowerCAmelCase__  (			unittest.TestCase    ):






  def 				__A (	self	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							str						)  ->							int:
      __lowerCamelCase					      =			list(tokenizer_class.max_model_input_sizes.keys()						)
      for model_name in sa_models[:1]:
          __lowerCamelCase					      =			tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__						)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE__						)
          self.assertIsInstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
          self.assertIsInstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

          for special_tok in tokenizer.all_special_tokens:
              self.assertIsInstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
              __lowerCamelCase					      =			tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__						)
              self.assertIsInstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)






  def 				__A (	self	:							Union[str, Any]							,    SCREAMING_SNAKE_CASE__	:							BatchEncoding							,    SCREAMING_SNAKE_CASE__	:							Optional[Callable] = None						)  ->							str:
      __lowerCamelCase					      =			pickle.dumps(SCREAMING_SNAKE_CASE__						)
      self.assertIsNotNone(SCREAMING_SNAKE_CASE__						)

      __lowerCamelCase					      =			pickle.loads(SCREAMING_SNAKE_CASE__						)

      # Ensure is_fast is correctly restored
      self.assertEqual(be_restored.is_fast							,    be_original.is_fast						)

      # Ensure encodings are potentially correctly restored
      if be_original.is_fast:
          self.assertIsNotNone(be_restored.encodings						)
      else:
          self.assertIsNone(be_restored.encodings						)

      # Ensure the keys are the same
      for original_v, restored_v in zip(be_original.values()							,    be_restored.values()						):
          if equal_op:
              self.assertTrue(equal_op(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)						)
          else:
              self.assertEqual(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)






  @slow
  def 				__A (	self	:							List[str]						)  ->							Tuple:
      self.check_tokenizer_from_pretrained(SCREAMING_SNAKE_CASE__						)






  def 				__A (	self	:							str						)  ->							str:
      self.assertEqual(TensorType('''tf'''						)							,    TensorType.TENSORFLOW						)
      self.assertEqual(TensorType('''pt'''						)							,    TensorType.PYTORCH						)
      self.assertEqual(TensorType('''np'''						)							,    TensorType.NUMPY						)






  @require_tokenizers
  def 				__A (	self	:							Dict						)  ->							Union[str, Any]:
      import numpy as np

      __lowerCamelCase					      =			BertTokenizer.from_pretrained('''bert-base-cased'''						)
      __lowerCamelCase					      =			BertTokenizerFast.from_pretrained('''bert-base-cased'''						)

      # Python no tensor
      with self.subTest('''BatchEncoding (Python, return_tensors=None)'''						):
          self.assert_dump_and_restore(tokenizer_p('''Small example to encode'''						)						)

      with self.subTest('''BatchEncoding (Python, return_tensors=NUMPY)'''						):
          self.assert_dump_and_restore(
              tokenizer_p('''Small example to encode'''							,    return_tensors=TensorType.NUMPY						)							,    np.array_equal						)

      with self.subTest('''BatchEncoding (Rust, return_tensors=None)'''						):
          self.assert_dump_and_restore(tokenizer_r('''Small example to encode'''						)						)

      with self.subTest('''BatchEncoding (Rust, return_tensors=NUMPY)'''						):
          self.assert_dump_and_restore(
              tokenizer_r('''Small example to encode'''							,    return_tensors=TensorType.NUMPY						)							,    np.array_equal						)






  @require_tf
  @require_tokenizers
  def 				__A (	self	:							str						)  ->							str:
      import tensorflow as tf

      def tf_array_equals(SCREAMING_SNAKE_CASE__	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							Optional[int]						):
          return tf.reduce_all(tf.equal(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)						)

      __lowerCamelCase					      =			BertTokenizer.from_pretrained('''bert-base-cased'''						)
      __lowerCamelCase					      =			BertTokenizerFast.from_pretrained('''bert-base-cased'''						)

      with self.subTest('''BatchEncoding (Python, return_tensors=TENSORFLOW)'''						):
          self.assert_dump_and_restore(
              tokenizer_p('''Small example to encode'''							,    return_tensors=TensorType.TENSORFLOW						)							,    SCREAMING_SNAKE_CASE__						)

      with self.subTest('''BatchEncoding (Rust, return_tensors=TENSORFLOW)'''						):
          self.assert_dump_and_restore(
              tokenizer_r('''Small example to encode'''							,    return_tensors=TensorType.TENSORFLOW						)							,    SCREAMING_SNAKE_CASE__						)






  @require_torch
  @require_tokenizers
  def 				__A (	self	:							List[str]						)  ->							Optional[Any]:
      import torch

      __lowerCamelCase					      =			BertTokenizer.from_pretrained('''bert-base-cased'''						)
      __lowerCamelCase					      =			BertTokenizerFast.from_pretrained('''bert-base-cased'''						)

      with self.subTest('''BatchEncoding (Python, return_tensors=PYTORCH)'''						):
          self.assert_dump_and_restore(
              tokenizer_p('''Small example to encode'''							,    return_tensors=TensorType.PYTORCH						)							,    torch.equal						)

      with self.subTest('''BatchEncoding (Rust, return_tensors=PYTORCH)'''						):
          self.assert_dump_and_restore(
              tokenizer_r('''Small example to encode'''							,    return_tensors=TensorType.PYTORCH						)							,    torch.equal						)






  @require_tokenizers
  def 				__A (	self	:							int						)  ->							str:
      __lowerCamelCase					      =			BertTokenizer.from_pretrained('''bert-base-cased'''						)
      __lowerCamelCase					      =			BertTokenizerFast.from_pretrained('''bert-base-cased'''						)

      with self.subTest('''Python Tokenizer'''						):
          self.assertFalse(tokenizer_p('''Small example to_encode'''						).is_fast						)

      with self.subTest('''Rust Tokenizer'''						):
          self.assertTrue(tokenizer_r('''Small example to_encode'''						).is_fast						)






  @require_tokenizers
  def 				__A (	self	:							int						)  ->							Optional[Any]:
      __lowerCamelCase					      =			BertTokenizerFast.from_pretrained('''bert-base-cased'''						)
      __lowerCamelCase					      =			tokenizer_r(['''Test''', '''\xad''', '''test''']							,    is_split_into_words=SCREAMING_SNAKE_CASE__						)

      self.assertEqual(encoded.word_to_tokens(0						)							,    TokenSpan(start=1							,    end=2						)						)
      self.assertEqual(encoded.word_to_tokens(1						)							,    SCREAMING_SNAKE_CASE__						)
      self.assertEqual(encoded.word_to_tokens(2						)							,    TokenSpan(start=2							,    end=3						)						)






  def 				__A (	self	:							Dict						)  ->							Optional[Any]:
      __lowerCamelCase					      =			BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}						)
      __lowerCamelCase					      =			batch.convert_to_tensors(tensor_type='''np'''						)
      self.assertEqual(tensor_batch['''inputs'''].shape							,    (2, 3)						)
      self.assertEqual(tensor_batch['''labels'''].shape							,    (2,)						)
      # test converting the converted
      with CaptureStderr() as cs:
          __lowerCamelCase					      =			batch.convert_to_tensors(tensor_type='''np'''						)
      self.assertFalse(len(cs.err						)							,    msg=f'''should have no warning, but got {cs.err}'''						)

      __lowerCamelCase					      =			BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}						)
      __lowerCamelCase					      =			batch.convert_to_tensors(tensor_type='''np'''							,    prepend_batch_axis=SCREAMING_SNAKE_CASE__						)
      self.assertEqual(tensor_batch['''inputs'''].shape							,    (1, 3)						)
      self.assertEqual(tensor_batch['''labels'''].shape							,    (1,)						)






  @require_torch
  def 				__A (	self	:							Optional[int]						)  ->							Optional[int]:
      __lowerCamelCase					      =			BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}						)
      __lowerCamelCase					      =			batch.convert_to_tensors(tensor_type='''pt'''						)
      self.assertEqual(tensor_batch['''inputs'''].shape							,    (2, 3)						)
      self.assertEqual(tensor_batch['''labels'''].shape							,    (2,)						)
      # test converting the converted
      with CaptureStderr() as cs:
          __lowerCamelCase					      =			batch.convert_to_tensors(tensor_type='''pt'''						)
      self.assertFalse(len(cs.err						)							,    msg=f'''should have no warning, but got {cs.err}'''						)

      __lowerCamelCase					      =			BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}						)
      __lowerCamelCase					      =			batch.convert_to_tensors(tensor_type='''pt'''							,    prepend_batch_axis=SCREAMING_SNAKE_CASE__						)
      self.assertEqual(tensor_batch['''inputs'''].shape							,    (1, 3)						)
      self.assertEqual(tensor_batch['''labels'''].shape							,    (1,)						)






  @require_tf
  def 				__A (	self	:							Optional[int]						)  ->							Optional[Any]:
      __lowerCamelCase					      =			BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}						)
      __lowerCamelCase					      =			batch.convert_to_tensors(tensor_type='''tf'''						)
      self.assertEqual(tensor_batch['''inputs'''].shape							,    (2, 3)						)
      self.assertEqual(tensor_batch['''labels'''].shape							,    (2,)						)
      # test converting the converted
      with CaptureStderr() as cs:
          __lowerCamelCase					      =			batch.convert_to_tensors(tensor_type='''tf'''						)
      self.assertFalse(len(cs.err						)							,    msg=f'''should have no warning, but got {cs.err}'''						)

      __lowerCamelCase					      =			BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}						)
      __lowerCamelCase					      =			batch.convert_to_tensors(tensor_type='''tf'''							,    prepend_batch_axis=SCREAMING_SNAKE_CASE__						)
      self.assertEqual(tensor_batch['''inputs'''].shape							,    (1, 3)						)
      self.assertEqual(tensor_batch['''labels'''].shape							,    (1,)						)






  @require_flax
  def 				__A (	self	:							Optional[Any]						)  ->							List[str]:
      __lowerCamelCase					      =			BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}						)
      __lowerCamelCase					      =			batch.convert_to_tensors(tensor_type='''jax'''						)
      self.assertEqual(tensor_batch['''inputs'''].shape							,    (2, 3)						)
      self.assertEqual(tensor_batch['''labels'''].shape							,    (2,)						)
      # test converting the converted
      with CaptureStderr() as cs:
          __lowerCamelCase					      =			batch.convert_to_tensors(tensor_type='''jax'''						)
      self.assertFalse(len(cs.err						)							,    msg=f'''should have no warning, but got {cs.err}'''						)

      __lowerCamelCase					      =			BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}						)
      __lowerCamelCase					      =			batch.convert_to_tensors(tensor_type='''jax'''							,    prepend_batch_axis=SCREAMING_SNAKE_CASE__						)
      self.assertEqual(tensor_batch['''inputs'''].shape							,    (1, 3)						)
      self.assertEqual(tensor_batch['''labels'''].shape							,    (1,)						)






  def 				__A (	self	:							List[Any]						)  ->							Optional[Any]:
      __lowerCamelCase					      =			[{'''input_ids''': np.array([0, 1, 2]						)}, {'''input_ids''': np.array([0, 1, 2, 3]						)}]
      __lowerCamelCase					      =			BertTokenizer.from_pretrained('''bert-base-cased'''						)

      __lowerCamelCase					      =			tokenizer.pad(SCREAMING_SNAKE_CASE__							,    padding=SCREAMING_SNAKE_CASE__						)
      self.assertTrue(isinstance(batch['''input_ids''']							,    np.ndarray						)						)
      self.assertEqual(batch['''input_ids'''].tolist()							,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)
      __lowerCamelCase					      =			tokenizer.pad(SCREAMING_SNAKE_CASE__							,    padding=SCREAMING_SNAKE_CASE__							,    return_tensors='''np'''						)
      self.assertTrue(isinstance(batch['''input_ids''']							,    np.ndarray						)						)
      self.assertEqual(batch['''input_ids'''].tolist()							,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)






  @require_torch
  def 				__A (	self	:							Optional[Any]						)  ->							str:
      import torch

      __lowerCamelCase					      =			[{'''input_ids''': torch.tensor([0, 1, 2]						)}, {'''input_ids''': torch.tensor([0, 1, 2, 3]						)}]
      __lowerCamelCase					      =			BertTokenizer.from_pretrained('''bert-base-cased'''						)

      __lowerCamelCase					      =			tokenizer.pad(SCREAMING_SNAKE_CASE__							,    padding=SCREAMING_SNAKE_CASE__						)
      self.assertTrue(isinstance(batch['''input_ids''']							,    torch.Tensor						)						)
      self.assertEqual(batch['''input_ids'''].tolist()							,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)
      __lowerCamelCase					      =			tokenizer.pad(SCREAMING_SNAKE_CASE__							,    padding=SCREAMING_SNAKE_CASE__							,    return_tensors='''pt'''						)
      self.assertTrue(isinstance(batch['''input_ids''']							,    torch.Tensor						)						)
      self.assertEqual(batch['''input_ids'''].tolist()							,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)






  @require_tf
  def 				__A (	self	:							List[str]						)  ->							Optional[int]:
      import tensorflow as tf

      __lowerCamelCase					      =			[{'''input_ids''': tf.constant([0, 1, 2]						)}, {'''input_ids''': tf.constant([0, 1, 2, 3]						)}]
      __lowerCamelCase					      =			BertTokenizer.from_pretrained('''bert-base-cased'''						)

      __lowerCamelCase					      =			tokenizer.pad(SCREAMING_SNAKE_CASE__							,    padding=SCREAMING_SNAKE_CASE__						)
      self.assertTrue(isinstance(batch['''input_ids''']							,    tf.Tensor						)						)
      self.assertEqual(batch['''input_ids'''].numpy().tolist()							,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)
      __lowerCamelCase					      =			tokenizer.pad(SCREAMING_SNAKE_CASE__							,    padding=SCREAMING_SNAKE_CASE__							,    return_tensors='''tf'''						)
      self.assertTrue(isinstance(batch['''input_ids''']							,    tf.Tensor						)						)
      self.assertEqual(batch['''input_ids'''].numpy().tolist()							,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)






  @require_tokenizers
  def 				__A (	self	:							Union[str, Any]						)  ->							Tuple:
      __lowerCamelCase					      =			Tokenizer(WordPiece(unk_token='''[UNK]'''						)						)
      PreTrainedTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE__						)







  @require_tokenizers
  def 				__A (	self	:							Optional[Any]						)  ->							Dict:
      __lowerCamelCase					      =			Tokenizer(WordPiece(unk_token='''[UNK]'''						)						)
      with tempfile.TemporaryDirectory() as tmpdirname:
          bert_tokenizer.save(os.path.join(SCREAMING_SNAKE_CASE__							,    '''tokenizer.json'''						)						)
          PreTrainedTokenizerFast(tokenizer_file=os.path.join(SCREAMING_SNAKE_CASE__							,    '''tokenizer.json'''						)						)





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						)						)



