
import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
     import torch


SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					logging.get_logger(__name__)

class      lowerCAmelCase__  :






  def __init__(	self	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Any							,    SCREAMING_SNAKE_CASE__	:							Tuple=21							,    SCREAMING_SNAKE_CASE__	:							int=[]							,    SCREAMING_SNAKE_CASE__	:							int=None						)  ->							int:
      __lowerCamelCase					      =			model
      __lowerCamelCase					      =			trace_batch_nums
      __lowerCamelCase					      =			abort_after_batch_num

      # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
      __lowerCamelCase					      =			collections.deque([]							,    __UpperCamelCase						)
      __lowerCamelCase					      =			[]
      __lowerCamelCase					      =			0
      __lowerCamelCase					      =			0
      __lowerCamelCase					      =			False
      __lowerCamelCase					      =			'''                 '''

      self.analyse_model()

      self.register_forward_hook()






  def 				__A (	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Any=None						)  ->							List[Any]:
      if frame is not None:
          self.expand_frame(__UpperCamelCase						)
      self.frames.append('''\n'''.join(self.frame						)						)
      __lowerCamelCase					      =			[]  # start a new frame






  def 				__A (	self	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]						)  ->							int:
      self.frame.append(__UpperCamelCase						)






  def 				__A (	self	:							Optional[Any]						)  ->							Any:
      print('''\n'''.join(self.frames						)						)
      __lowerCamelCase					      =			[]






  def 				__A (	self	:							int						)  ->							List[Any]:
      __lowerCamelCase					      =			[]






  def 				__A (	self	:							Any						)  ->							Optional[Any]:
      print(f'''\nDetected inf/nan during batch_number={self.batch_number}'''						)
      print(f'''Last {len(self.frames						)} forward frames:'''						)
      print(f'''{'abs min':8} {'abs max':8} metadata'''						)
      print('''\n'''.join(self.frames						)						)
      print('''\n\n'''						)
      __lowerCamelCase					      =			[]






  def 				__A (	self	:							List[Any]						)  ->							Optional[int]:
      # extract the fully qualified module names, to be able to report at run time. e.g.:
      # encoder.block.2.layer.0.SelfAttention.o
      #
      # for shared weights only the first shared module name will be registered
      __lowerCamelCase					      =			{m: name for name, m in self.model.named_modules()}
      # self.longest_module_name = max(len(v) for v in self.module_names.values())






  def 				__A (	self	:							str							,    SCREAMING_SNAKE_CASE__	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							Optional[Any]:
      if torch.is_tensor(__UpperCamelCase						):
          self.expand_frame(get_abs_min_max(__UpperCamelCase							,    __UpperCamelCase						)						)
          if detect_overflow(__UpperCamelCase							,    __UpperCamelCase						):
              __lowerCamelCase					      =			True
      elif var is None:
          self.expand_frame(f'''{'None':>17} {ctx}'''						)
      else:
          self.expand_frame(f'''{'not a tensor':>17} {ctx}'''						)






  def 				__A (	self	:							Dict						)  ->							Dict:
      self.expand_frame(f'''\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'''						)
      self.expand_frame(f'''{'abs min':8} {'abs max':8} metadata'''						)






  def 				__A (	self	:							Tuple						)  ->							Dict:
      self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''						)






  def 				__A (	self	:							Any							,    SCREAMING_SNAKE_CASE__	:							int							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							Any						)  ->							int:
      self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''						)

      # params
      for name, p in module.named_parameters(recurse=__UpperCamelCase						):
          self.analyse_variable(__UpperCamelCase							,    __UpperCamelCase						)

      # inputs
      if isinstance(__UpperCamelCase							,    __UpperCamelCase						):
          for i, x in enumerate(__UpperCamelCase						):
              self.analyse_variable(__UpperCamelCase							,    f'''input[{i}]'''						)
      else:
          self.analyse_variable(__UpperCamelCase							,    '''input'''						)

      # outputs
      if isinstance(__UpperCamelCase							,    __UpperCamelCase						):
          for i, x in enumerate(__UpperCamelCase						):
              # possibly a tuple of tuples
              if isinstance(__UpperCamelCase							,    __UpperCamelCase						):
                  for j, y in enumerate(__UpperCamelCase						):
                      self.analyse_variable(__UpperCamelCase							,    f'''output[{i}][{j}]'''						)
              else:
                  self.analyse_variable(__UpperCamelCase							,    f'''output[{i}]'''						)
      else:
          self.analyse_variable(__UpperCamelCase							,    '''output'''						)

      self.save_frame()






  def 				__A (	self	:							Optional[int]						)  ->							int:
      self.model.apply(self._register_forward_hook						)






  def 				__A (	self	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							List[Any]:
      module.register_forward_hook(self.forward_hook						)







  def 				__A (	self	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]						)  ->							List[str]:
      # - input is a tuple of packed inputs (could be non-Tensors)
      # - output could be a Tensor or a tuple of Tensors and non-Tensors

      __lowerCamelCase					      =			False

      __lowerCamelCase					      =			True if self.batch_number in self.trace_batch_nums else False
      if trace_mode:
          self.reset_saved_frames()

      if self.total_calls == 0:
          self.batch_start_frame()
      self.total_calls += 1

      # count batch numbers - the very first forward hook of the batch will be called when the
      # batch completes - i.e. it gets called very last - we know this batch has finished
      if module == self.model:
          self.batch_number += 1
          __lowerCamelCase					      =			True

      self.create_frame(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase						)

      # if last_frame_of_batch:
      #     self.batch_end_frame()

      if trace_mode:
          self.trace_frames()

      if last_frame_of_batch:
          self.batch_start_frame()

      if self.detected_overflow and not trace_mode:
          self.dump_saved_frames()

          # now we can abort, as it's pointless to continue running
          raise ValueError(
              '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
              '''Please scroll up above this traceback to see the activation values prior to this event.'''						)

      # abort after certain batch if requested to do so
      if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
          raise ValueError(
              f'''DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'''
              f''' `abort_after_batch_num={self.abort_after_batch_num}` arg'''						)





def 				__magic_name__				(     __lowerCAmelCase			:		List[Any]	,						__lowerCAmelCase			:		List[Any]							)    ->     Optional[Any]:
    __lowerCamelCase					      =			var.abs()
    return f'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''




def 				__magic_name__				(     __lowerCAmelCase			:		Dict	,						__lowerCAmelCase			:		Dict							)    ->     Optional[Any]:
    __lowerCamelCase					      =			False
    if torch.isnan(lowercase_							).any().item():
        __lowerCamelCase					      =			True
        print(f'''{ctx} has nans'''							)
    if torch.isinf(lowercase_							).any().item():
        __lowerCamelCase					      =			True
        print(f'''{ctx} has infs'''							)

    # if needed to monitor large elements can enable the following
    if 0:  # and detected:
        __lowerCamelCase					      =			var[torch.ge(var.abs()	,						100							)]
        if naaa.numel() > 0:
            print(f'''{ctx}:  n100={naaa.numel()}'''							)
        __lowerCamelCase					      =			var[torch.ge(var.abs()	,						1000							)]
        if naaaa.numel() > 0:
            print(f'''{ctx}: n1000={naaaa.numel()}'''							)
        __lowerCamelCase					      =			var[torch.ge(var.abs()	,						1_0000							)]
        if naaaaa.numel() > 0:
            print(f'''{ctx}: n10000={naaaaa.numel()}'''							)

    if 0:
        print(f'''min={var.min():9.2e} max={var.max():9.2e}'''							)

    if 0:
        print(f'''min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})'''							)

    return detected







class      lowerCAmelCase__  (			UpperCAmelCase_    ):
  a__ :		List[Any]   						=	"""underflow_overflow"""
  a__ :		Any   						=	"""tpu_metrics_debug"""





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						)						)



