
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


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

lowercase		:    Union[str, Any]			=       {
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}




class  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    '''mobilevitv2'''






							def __init__(       self    :      Any    ,			snake_case__    :      str=3    ,			snake_case__    :      Optional[int]=2_5_6    ,			snake_case__    :      Tuple=2    ,			snake_case__    :      List[Any]=2.0    ,			snake_case__    :      Tuple="swish"    ,			snake_case__    :      Any=3    ,			snake_case__    :      str=3_2    ,			snake_case__    :      Any=0.1    ,			snake_case__    :      Optional[Any]=0.02    ,			snake_case__    :      List[str]=1e-5    ,			snake_case__    :      int=5_1_2    ,			snake_case__    :      Dict=[6, 1_2, 1_8]    ,			snake_case__    :      Optional[int]=0.1    ,			snake_case__    :      Tuple=2_5_5    ,			snake_case__    :      List[Any]=[2, 4, 3]    ,			snake_case__    :      int=[1_2_8, 1_9_2, 2_5_6]    ,			snake_case__    :      Tuple=1.0    ,			snake_case__    :      Any=2    ,			snake_case__    :      Optional[Any]=0.0    ,			snake_case__    :      Optional[int]=0.0    ,			**snake_case__    :      int    ,			):






												"""simple docstring"""



												super().__init__(**snake_case__     )

												__a:    List[str]		     =       num_channels
												__a:    Dict		     =       image_size
												__a:    Dict		     =       patch_size
												__a:    Optional[int]		     =       expand_ratio
												__a:    Optional[int]		     =       hidden_act
												__a:    Union[str, Any]		     =       conv_kernel_size
												__a:    Optional[Any]		     =       output_stride
												__a:    Dict		     =       initializer_range
												__a:    List[Any]		     =       layer_norm_eps
												__a:    Optional[int]		     =       n_attn_blocks
												__a:    List[str]		     =       base_attn_unit_dims
												__a:    Dict		     =       width_multiplier
												__a:    int		     =       ffn_multiplier
												__a:    Optional[Any]		     =       ffn_dropout
												__a:    List[str]		     =       attn_dropout
												__a:    Tuple		     =       classifier_dropout_prob

												# decode head attributes for semantic segmentation
												__a:    Any		     =       aspp_out_channels
												__a:    Union[str, Any]		     =       atrous_rates
												__a:    Any		     =       aspp_dropout_prob
												__a:    List[Any]		     =       semantic_loss_ignore_index






class  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    version.parse('''1.11'''							)






							@property
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]     )






							@property
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												if self.task == "image-classification":
																	return OrderedDict([("""logits""", {0: """batch"""})]     )
												else:
																	return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]     )






							@property
							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												return 1e-4


"""simple docstring"""
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch




class  _snake_case	(    unittest.TestCase							):






							@patch("""sys.argv"""    ,			["""fakeprogrampath""", """env"""]     )
							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												with CaptureStd() as cs:
																	transformers.commands.transformers_cli.main()
												self.assertIn("""Python version"""    ,			cs.out     )
												self.assertIn("""Platform"""    ,			cs.out     )
												self.assertIn("""Using distributed or parallel set-up in script?"""    ,			cs.out     )






							@is_pt_tf_cross_test
							@patch(
							    """sys.argv"""    ,			["""fakeprogrampath""", """pt-to-tf""", """--model-name""", """hf-internal-testing/tiny-random-gptj""", """--no-pr"""]     )
							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												shutil.rmtree("""/tmp/hf-internal-testing/tiny-random-gptj"""    ,			ignore_errors=snake_case__     )  # cleans potential past runs
												transformers.commands.transformers_cli.main()

												# The original repo has no TF weights -- if they exist, they were created by the CLI
												self.assertTrue(os.path.exists("""/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"""     )     )






							@require_torch
							@patch("""sys.argv"""    ,			["""fakeprogrampath""", """download""", """hf-internal-testing/tiny-random-gptj""", """--cache-dir""", """/tmp"""]     )
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												# # remove any previously downloaded model to start clean
												shutil.rmtree("""/tmp/models--hf-internal-testing--tiny-random-gptj"""    ,			ignore_errors=snake_case__     )

												# run the command
												transformers.commands.transformers_cli.main()

												# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"""     )     )
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/refs"""     )     )
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"""     )     )






							@require_torch
							@patch(
							    """sys.argv"""    ,			[
							        """fakeprogrampath""",
							        """download""",
							        """hf-internal-testing/test_dynamic_model_with_tokenizer""",
							        """--trust-remote-code""",
							        """--cache-dir""",
							        """/tmp""",
							    ]    ,			)
							def 				__lowercase	(       self    :      Union[str, Any]     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												# # remove any previously downloaded model to start clean
												shutil.rmtree("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"""    ,			ignore_errors=snake_case__     )

												# run the command
												transformers.commands.transformers_cli.main()

												# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"""     )     )
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"""     )     )
												self.assertTrue(
												    os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"""     )     )
