

import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
					import jax
					import jax.numpy as jnp

					from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor







class  lowercase				(       unittest.TestCase			):




				def __init__(					self      ,   snake_case      ,   snake_case=3      ,   snake_case=32      ,   snake_case=3      ,   snake_case=10      ,   snake_case=[10, 20, 30, 40]      ,   snake_case=[1, 1, 2, 1]      ,   snake_case=True      ,   snake_case=True      ,   snake_case="relu"      ,   snake_case=3      ,   snake_case=None      ,   ):
											snake_case_							=    parent
											snake_case_							=    batch_size
											snake_case_							=    image_size
											snake_case_							=    num_channels
											snake_case_							=    embeddings_size
											snake_case_							=    hidden_sizes
											snake_case_							=    depths
											snake_case_							=    is_training
											snake_case_							=    use_labels
											snake_case_							=    hidden_act
											snake_case_							=    num_labels
											snake_case_							=    scope
											snake_case_							=    len(snake_case       )




				def     a							(					self       ):
											snake_case_							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

											snake_case_							=    self.get_config()

											return config, pixel_values




				def     a							(					self       ):
											return RegNetConfig(
											    num_channels=self.num_channels      ,   embeddings_size=self.embeddings_size      ,   hidden_sizes=self.hidden_sizes      ,   depths=self.depths      ,   hidden_act=self.hidden_act      ,   num_labels=self.num_labels      ,   image_size=self.image_size      ,   )




				def     a							(					self      ,   snake_case      ,   snake_case       ):
											snake_case_							=    FlaxRegNetModel(config=snake_case       )
											snake_case_							=    model(snake_case       )

											# Output shape (b, c, h, w)
											self.parent.assertEqual(
											    result.last_hidden_state.shape      ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,   )




				def     a							(					self      ,   snake_case      ,   snake_case       ):
											snake_case_							=    self.num_labels
											snake_case_							=    FlaxRegNetForImageClassification(config=snake_case       )
											snake_case_							=    model(snake_case       )
											self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels)       )




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







@require_flax
class  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: Dict       			=			(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			False
				__SCREAMING_SNAKE_CASE					: int       			=			False
				__SCREAMING_SNAKE_CASE					: int       			=			False




				def     a							(					self       ):
											snake_case_							=    FlaxRegNetModelTester(self       )
											snake_case_							=    ConfigTester(self      ,   config_class=snake_case      ,   has_text_modality=snake_case       )




				def     a							(					self       ):
											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()




				def     a							(					self       ):
											return




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case       )




				@unittest.skip(reason='RegNet does not use inputs_embeds'       )
				def     a							(					self       ):
											pass




				@unittest.skip(reason='RegNet does not support input and output embeddings'       )
				def     a							(					self       ):
											pass




				def     a							(					self       ):
											snake_case_				,    snake_case_							=    self.model_tester.prepare_config_and_inputs_for_common()

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

																		snake_case_							=    ['pixel_values']
																		self.assertListEqual(arg_names[:1]      ,   snake_case       )




				def     a							(					self       ):
											def check_hidden_states_output(snake_case      ,   snake_case      ,   snake_case       ):
																		snake_case_							=    model_class(snake_case       )

																		snake_case_							=    model(**self._prepare_for_class(snake_case      ,   snake_case       )       )

																		snake_case_							=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		snake_case_							=    self.model_tester.num_stages
																		self.assertEqual(len(snake_case       )      ,   expected_num_stages + 1       )

											snake_case_				,    snake_case_							=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		snake_case_							=    True
																		check_hidden_states_output(snake_case      ,   snake_case      ,   snake_case       )

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

																		check_hidden_states_output(snake_case      ,   snake_case      ,   snake_case       )




				def     a							(					self       ):
											snake_case_				,    snake_case_							=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__       ):
																									snake_case_							=    self._prepare_for_class(snake_case      ,   snake_case       )
																									snake_case_							=    model_class(snake_case       )

																									@jax.jit
																									def model_jitted(snake_case      ,   **snake_case       ):
																																return model(pixel_values=snake_case      ,   **snake_case       )

																									with self.subTest('JIT Enabled'       ):
																																snake_case_							=    model_jitted(**snake_case       ).to_tuple()

																									with self.subTest('JIT Disabled'       ):
																																with jax.disable_jit():
																																							snake_case_							=    model_jitted(**snake_case       ).to_tuple()

																									self.assertEqual(len(snake_case       )      ,   len(snake_case       )       )
																									for jitted_output, output in zip(snake_case      ,   snake_case       ):
																																self.assertEqual(jitted_output.shape      ,   output.shape       )





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
							return image







@require_flax
class  lowercase				(       unittest.TestCase			):




				@cached_property
				def     a							(					self       ):
											return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'       ) if is_vision_available() else None




				@slow
				def     a							(					self       ):
											snake_case_							=    FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'       )

											snake_case_							=    self.default_image_processor
											snake_case_							=    prepare_img()
											snake_case_							=    image_processor(images=snake_case      ,   return_tensors='np'       )

											snake_case_							=    model(**snake_case       )

											# verify the logits
											snake_case_							=    (1, 1000)
											self.assertEqual(outputs.logits.shape      ,   snake_case       )

											snake_case_							=    jnp.array([-0.41_80, -1.50_51, -3.48_36]       )

											self.assertTrue(jnp.allclose(outputs.logits[0, :3]      ,   snake_case      ,   atol=1e-4       )       )







import os

import numpy
import onnx





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    a.name
							snake_case_							=    b.name

							snake_case_							=    ''
							snake_case_							=    ''

							snake_case_							=    a == b

							snake_case_							=    name_a
							snake_case_							=    name_b

							return res





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i, input_name in enumerate(node_proto.input      ):
														if input_name == name:
																					node_proto.input.insert(UpperCamelCase__   ,							UpperCamelCase__      )
																					node_proto.input.pop(i + 1      )

							if node_proto.op_type == "If":
														_graph_replace_input_with(node_proto.attribute[0].g   ,							UpperCamelCase__   ,							UpperCamelCase__      )
														_graph_replace_input_with(node_proto.attribute[1].g   ,							UpperCamelCase__   ,							UpperCamelCase__      )
							if node_proto.op_type == "Loop":
														_graph_replace_input_with(node_proto.attribute[0].g   ,							UpperCamelCase__   ,							UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for n in graph_proto.node:
														_node_replace_input_with(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    list(model.graph.initializer      )
							snake_case_							=    list(model_without_ext.graph.initializer      )
							for i, ref_i in ind_to_replace:
														assert inits_with_data[i].name == inits[i].name
														assert inits_with_data[ref_i].name == inits[ref_i].name
														assert i > ref_i

														snake_case_							=    inits[i].name
														snake_case_							=    inits[ref_i].name

														model_without_ext.graph.initializer.remove(inits[i]      )

														# for n in model.graph.node:
														_graph_replace_input_with(model_without_ext.graph   ,							UpperCamelCase__   ,							UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    os.path.dirname(UpperCamelCase__      )
							snake_case_							=    os.path.basename(UpperCamelCase__      )

							snake_case_							=    onnx.load(os.path.join(UpperCamelCase__   ,							UpperCamelCase__      )      )

							snake_case_							=    list(model.graph.initializer      )

							snake_case_							=    set()
							snake_case_							=    {}
							snake_case_							=    []

							snake_case_							=    0

							for i in range(len(UpperCamelCase__      )      ):
														if i in dup_set:
																					continue

														for j in range(i + 1   ,							len(UpperCamelCase__      )      ):
																					if j in dup_set:
																												continue
																					if _is_equal_tensor_proto(inits[i]   ,							inits[j]      ):
																												dup_set.add(UpperCamelCase__      )
																												dup_set.add(UpperCamelCase__      )

																												snake_case_							=    inits[j].data_type
																												snake_case_							=    numpy.prod(inits[j].dims      )
																												if dtype == 1:
																																			mem_size *= 4
																												elif dtype == 6:
																																			mem_size *= 4
																												elif dtype == 7 or dtype == 11:
																																			mem_size *= 8
																												else:
																																			print('unexpected data type: '   ,							UpperCamelCase__      )
																												total_reduced_size += mem_size

																												snake_case_							=    inits[i].name
																												snake_case_							=    inits[j].name

																												if name_i in dup_map:
																																			dup_map[name_i].append(UpperCamelCase__      )
																												else:
																																			snake_case_							=    [name_j]
																												ind_to_replace.append((j, i)      )

							print('total reduced size: '   ,							total_reduced_size / 1024 / 1024 / 1024   ,							'GB'      )

							snake_case_							=    sorted(UpperCamelCase__      )
							_remove_dup_initializers_from_model(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )

							snake_case_							=    'optimized_' + model_file_name
							snake_case_							=    os.path.join(UpperCamelCase__   ,							UpperCamelCase__      )
							onnx.save(UpperCamelCase__   ,							UpperCamelCase__      )

							return new_model




