




'''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   _snake_case:






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






    """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 	_UpperCamelCase   (self					:     Dict						)					->      Dict:






    """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 	_UpperCamelCase   (self					:     List[Any]						)					->      Tuple:






    """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 	_UpperCamelCase   (self					:     int						,		a					:     Tuple						,		a					:     int						)					->      Dict:






    """simple docstring"""


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






 def 	_UpperCamelCase   (self					:     List[str]						)					->      Dict:






    """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   _snake_case( UpperCAmelCase					,   UpperCAmelCase					,   unittest.TestCase			):
 __snake_case:       Dict									=				(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
 __snake_case:       Dict									=				{'''feature-extraction''': MgpstrForSceneTextRecognition} if is_torch_available() else {}
 __snake_case:       Optional[int]									=				False

 __snake_case:       Dict									=				False
 __snake_case:       Dict									=				False
 __snake_case:       Optional[int]									=				False
 __snake_case:       int									=				False






 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      Any:






    """simple docstring"""


    A__         =    MgpstrModelTester(self						)
    A__         =    ConfigTester(self						,		config_class=a						,		has_text_modality=a						)






 def 	_UpperCamelCase   (self					:     Tuple						)					->      List[Any]:






    """simple docstring"""


    self.config_tester.run_common_tests()






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






    """simple docstring"""


    A__         =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a						)






 @unittest.skip(reason='MgpstrModel does not use inputs_embeds'						)
 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      Union[str, Any]:






    """simple docstring"""


    pass






 def 	_UpperCamelCase   (self					:     List[Any]						)					->      Optional[int]:






    """simple docstring"""


    A__     , A__         =    self.model_tester.prepare_config_and_inputs_for_common()

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






 def 	_UpperCamelCase   (self					:     Dict						)					->      Optional[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(a						)
       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]						,		a						)






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






    """simple docstring"""


    pass






 def 	_UpperCamelCase   (self					:     List[str]						)					->      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(a						)
       self.assertTrue(model.is_gradient_checkpointing						)






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






    """simple docstring"""


    def check_hidden_states_output(a					:     int						,		a					:     Dict						,		a					:     str						):
       A__         =    model_class(a						)
       model.to(a						)
       model.eval()

       with torch.no_grad():
          A__         =    model(**self._prepare_for_class(a						,		a						)						)

       A__         =    outputs.hidden_states

       A__         =    getattr(
           self.model_tester						,		'expected_num_hidden_layers'						,		self.model_tester.num_hidden_layers + 1						)
       self.assertEqual(len(a						)						,		a						)

       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(a						,		a						,		a						)

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

       check_hidden_states_output(a						,		a						,		a						)






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






    """simple docstring"""


    A__     , A__         =    self.model_tester.prepare_config_and_inputs_for_common()

    A__         =    _config_zero_init(a						)
    for model_class in self.all_model_classes:
       A__         =    model_class(config=a						)
       for name, param in model.named_parameters():
          if isinstance(a						,		(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					:     Optional[int]						)					->      Union[str, Any]:






    """simple docstring"""


    pass







def 	_A			(   ):






   '''simple docstring'''







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







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






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






    """simple docstring"""


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

    A__         =    prepare_img()
    A__         =    processor(images=a						,		return_tensors='pt'						).pixel_values.to(a						)

    # forward pass
    with torch.no_grad():
       A__         =    model(a						)

    # 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]						,		a						)

    A__         =    torch.tensor(
        [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]						,		device=a						,		)

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








'''simple docstring'''







from typing import Any

import numpy as np

class   _snake_case:






 def __init__(self					:     int						,		a					:     np.ndarray						,		a					:     int						)					->      None:






    """simple docstring"""


    if np.any(tableau[:, -1]						,		where=tableau[:, -1] < 0						):
       raise ValueError('RHS must be > 0'						)

    A__         =    tableau
    A__     , A__         =    tableau.shape

    # Number of decision variables x1, x2, x3...
    A__         =    n_vars

    # Number of artificial variables to be minimised
    A__         =    len(np.where(tableau[self.n_vars : -1] == -1						)[0]						)

    # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
    A__         =    (self.n_art_vars > 0) + 1

    # Number of slack variables added to make inequalities into equalities
    A__         =    self.n_rows - self.n_stages

    # Objectives for each stage
    A__         =    ['max']

    # In two stage simplex, first minimise then maximise
    if self.n_art_vars:
       self.objectives.append('min'						)

    A__         =    ['']

    # Index of current pivot row and column
    A__         =    None
    A__         =    None

    # Does objective row only contain (non)-negative values?
    A__         =    False






 @staticmethod
 def 	_UpperCamelCase   (*a					:     int						)					->      list[str]:






    """simple docstring"""


    if len(a						) != 3:
       raise ValueError('Must provide n_vars, n_slack, and n_art_vars'						)

    if not all(x >= 0 and isinstance(a						,		a						) for x in args						):
       raise ValueError('All arguments must be non-negative integers'						)

    # decision | slack | artificial
    A__         =    ['x', 's', 'a']
    A__         =    []
    for i in range(3						):
       for j in range(args[i]						):
          titles.append(string_starts[i] + str(j + 1						)						)
    titles.append('RHS'						)
    return titles






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     np.ndarray						)					->      tuple[Any, Any]:






    """simple docstring"""


    A__         =    self.objectives[-1]

    # Find entries of highest magnitude in objective rows
    A__         =    (objective == 'min') - (objective == 'max')
    A__         =    np.argmax(sign * tableau[0, : self.n_vars]						)

    # Choice is only valid if below 0 for maximise, and above for minimise
    if sign * self.tableau[0, col_idx] <= 0:
       A__         =    True
       return 0, 0

    # Pivot row is chosen as having the lowest quotient when elements of
    # the pivot column divide the right-hand side

    # Slice excluding the objective rows
    A__         =    slice(self.n_stages						,		self.n_rows						)

    # RHS
    A__         =    tableau[s, -1]

    # Elements of pivot column within slice
    A__         =    tableau[s, col_idx]

    # Array filled with nans
    A__         =    np.full(self.n_rows - self.n_stages						,		np.nan						)

    # If element in pivot column is greater than zeron_stages, return
    # quotient or nan otherwise
    A__         =    np.divide(a						,		a						,		out=a						,		where=divisor > 0						)

    # Arg of minimum quotient excluding the nan values. n_stages is added
    # to compensate for earlier exclusion of objective columns
    A__         =    np.nanargmin(a						) + self.n_stages
    return row_idx, col_idx






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     np.ndarray						,		a					:     int						,		a					:     int						)					->      np.ndarray:






    """simple docstring"""


    A__         =    tableau[row_idx].copy()

    A__         =    piv_row[col_idx]

    # Entry becomes 1
    piv_row *= 1 / piv_val

    # Variable in pivot column becomes basic, ie the only non-zero entry
    for idx, coeff in enumerate(tableau[:, col_idx]						):
       tableau[idx] += -coeff * piv_row
    A__         =    piv_row
    return tableau






 def 	_UpperCamelCase   (self					:     Union[str, Any]						,		a					:     np.ndarray						)					->      np.ndarray:






    """simple docstring"""


    self.objectives.pop()

    if not self.objectives:
       return tableau

    # Slice containing ids for artificial columns
    A__         =    slice(-self.n_art_vars - 1						,		-1						)

    # Delete the artificial variable columns
    A__         =    np.delete(a						,		a						,		axis=1						)

    # Delete the objective row of the first stage
    A__         =    np.delete(a						,		0						,		axis=0						)

    A__         =    1
    self.n_rows -= 1
    A__         =    0
    A__         =    False
    return tableau






 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      dict[Any, Any]:






    """simple docstring"""


    for _ in range(1_00						):
       # Completion of each stage removes an objective. If both stages
       # are complete, then no objectives are left
       if not self.objectives:
          A__         =    self.generate_col_titles(
              self.n_vars						,		self.n_slack						,		self.n_art_vars						)

          # Find the values of each variable at optimal solution
          return self.interpret_tableau(self.tableau						,		self.col_titles						)

       A__     , A__         =    self.find_pivot(self.tableau						)

       # If there are no more negative values in objective row
       if self.stop_iter:
          # Delete artificial variable columns and rows. Update attributes
          A__         =    self.change_stage(self.tableau						)
       else:
          A__         =    self.pivot(self.tableau						,		a						,		a						)
    return {}






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     np.ndarray						,		a					:     list[str]						)					->      dict[str, float]:






    """simple docstring"""


    A__         =    {'P': abs(tableau[0, -1]						)}

    for i in range(self.n_vars						):
       # Gives ids of nonzero entries in the ith column
       A__         =    np.nonzero(tableau[:, i]						)
       A__         =    len(nonzero[0]						)

       # First entry in the nonzero ids
       A__         =    nonzero[0][0]
       A__         =    tableau[nonzero_rowidx, i]

       # If there is only one nonzero value in column, which is one
       if n_nonzero == nonzero_val == 1:
          A__         =    tableau[nonzero_rowidx, -1]
          A__         =    rhs_val

        # Check for basic variables
    for title in col_titles:
       # Don't add RHS or slack variables to output dict
       if title[0] not in "R-s-a":
          output_dict.setdefault(a						,		0						)
    return output_dict


if __name__ == "__main__":
    import doctest

    doctest.testmod()


