import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase						:			List[Any]     		=	logging.get_logger()





@dataclass
class    lowercase__		:
						UpperCamelCase_	=						42
						UpperCamelCase_	=						field(default_factory=a__)
						UpperCamelCase_	=						field(default_factory=a__)


						def 			__A			(			self      :							Any       ,						UpperCamelCase__      :							List[Any]       ,						UpperCamelCase__      :							Dict       ,						UpperCamelCase__      :							Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Dict     							=				len(list(m.modules()		)		) == 1 or isinstance(_UpperCAmelCase       ,						nn.Convad		) or isinstance(_UpperCAmelCase       ,						nn.BatchNormad		)
												if has_not_submodules:
																		self.traced.append(_UpperCAmelCase		)


						def __call__(			self      :							Tuple       ,						UpperCamelCase__      :							List[str]		):





												'''simple docstring'''





												for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook		)		)
												self.module(_UpperCAmelCase		)
												[x.remove() for x in self.handles]
												return self



						@property
						def 			__A			(			self      :							Dict		):





												'''simple docstring'''





												return list(filter(lambda UpperCamelCase__		: len(list(x.state_dict().keys()		)		) > 0       ,						self.traced		)		)






@dataclass
class    lowercase__		:
						UpperCamelCase_	=						42
						UpperCamelCase_	=						42
						UpperCamelCase_	=						1
						UpperCamelCase_	=						field(default_factory=a__)
						UpperCamelCase_	=						field(default_factory=a__)
						UpperCamelCase_	=						True


						def __call__(			self      :							List[Any]       ,						UpperCamelCase__      :							List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				Tracker(self.dest		)(_UpperCAmelCase		).parametrized
												SCREAMING_SNAKE_CASE				:       Any     							=				Tracker(self.src		)(_UpperCAmelCase		).parametrized

												SCREAMING_SNAKE_CASE				:       int     							=				list(filter(lambda UpperCamelCase__		: type(_UpperCAmelCase		) not in self.src_skip       ,						_UpperCAmelCase		)		)
												SCREAMING_SNAKE_CASE				:       Tuple     							=				list(filter(lambda UpperCamelCase__		: type(_UpperCAmelCase		) not in self.dest_skip       ,						_UpperCAmelCase		)		)

												if len(_UpperCAmelCase		) != len(_UpperCAmelCase		) and self.raise_if_mismatch:
																		raise Exception(
																		    f"""Numbers of operations are different. Source module has {len(_UpperCAmelCase		)} operations while"""
																		    f""" destination module has {len(_UpperCAmelCase		)}."""		)

												for dest_m, src_m in zip(_UpperCAmelCase       ,						_UpperCAmelCase		):
																		dest_m.load_state_dict(src_m.state_dict()		)
																		if self.verbose == 1:
																								print(f"""Transfered from={src_m} to={dest_m}"""		)






class    lowercase__		(  nn.Module):


						def __init__(			self      :							List[str]       ,						UpperCamelCase__      :							List[Any]		):





												'''simple docstring'''





												super().__init__()

												SCREAMING_SNAKE_CASE				:       List[Tuple[str, nn.Module]]     							=				[]
												# - get the stem
												feature_blocks.append(('''conv1''', model.stem)		)
												# - get all the feature blocks
												for k, v in model.trunk_output.named_children():
																		assert k.startswith('''block'''		), f"""Unexpected layer name {k}"""
																		SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				len(_UpperCAmelCase		) + 1
																		feature_blocks.append((f"""res{block_index}""", v)		)

												SCREAMING_SNAKE_CASE				:       List[str]     							=				nn.ModuleDict(_UpperCAmelCase		)



						def 			__A			(			self      :							int       ,						UpperCamelCase__      :							Any		):





												'''simple docstring'''





												return get_trunk_forward_outputs(
												    _UpperCAmelCase       ,						out_feat_keys=_UpperCAmelCase       ,						feature_blocks=self._feature_blocks       ,						)






class    lowercase__		(  a__):


						def 			__A			(			self      :							Union[str, Any]       ,						UpperCamelCase__      :							int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       str     							=				x.split('''-'''		)
												return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]		)



						def __getitem__(			self      :							Union[str, Any]       ,						UpperCamelCase__      :							Optional[int]		):





												'''simple docstring'''





												if x not in self:
																		SCREAMING_SNAKE_CASE				:       Dict     							=				self.convert_name_to_timm(_UpperCAmelCase		)
																		SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				partial(lambda: (timm.create_model(_UpperCAmelCase       ,						pretrained=_UpperCAmelCase		).eval(), None)		)

												else:
																		SCREAMING_SNAKE_CASE				:       Tuple     							=				super().__getitem__(_UpperCAmelCase		)

												return val






class    lowercase__		(  a__):


						def __getitem__(			self      :							Optional[int]       ,						UpperCamelCase__      :							List[str]		):





												'''simple docstring'''





												if "seer" in x and "in1k" not in x:
																		SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				RegNetModel
												else:
																		SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				RegNetForImageClassification
												return val






def  A      (       _lowercase						,    _lowercase						,    _lowercase		):
						for from_key, to_key in keys:
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				from_state_dict[from_key].clone()
												print(f"""Copied key={from_key} to={to_key}"""		)
						return to_state_dict
def  A      (       _lowercase						,    _lowercase						,    _lowercase						,    _lowercase						,    _lowercase						,    _lowercase = True						,    ):
						print(f"""Converting {name}..."""		)
						with torch.no_grad():
												SCREAMING_SNAKE_CASE				:       Dict     							=				from_model_func()
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				our_model_func(__snake_case		).eval()
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				ModuleTransfer(src=__snake_case						,    dest=__snake_case						,    raise_if_mismatch=__snake_case		)
												SCREAMING_SNAKE_CASE				:       int     							=				torch.randn((1, 3, 224, 224)		)
												module_transfer(__snake_case		)

						if from_state_dict is not None:
												SCREAMING_SNAKE_CASE				:       int     							=				[]
												# for seer - in1k finetuned we have to manually copy the head
												if "seer" in name and "in1k" in name:
																		SCREAMING_SNAKE_CASE				:       str     							=				[('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
												SCREAMING_SNAKE_CASE				:       Tuple     							=				manually_copy_vissl_head(__snake_case						,    our_model.state_dict()						,    __snake_case		)
												our_model.load_state_dict(__snake_case		)

						SCREAMING_SNAKE_CASE				:       Tuple     							=				our_model(__snake_case						,    output_hidden_states=__snake_case		)
						SCREAMING_SNAKE_CASE				:       Optional[int]     							=				(
						    our_outputs.logits if isinstance(__snake_case						,    __snake_case		) else our_outputs.last_hidden_state
						)

						SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				from_model(__snake_case		)
						SCREAMING_SNAKE_CASE				:       int     							=				from_output[-1] if type(__snake_case		) is list else from_output

						# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
						if "seer" in name and "in1k" in name:
												SCREAMING_SNAKE_CASE				:       List[Any]     							=				our_outputs.hidden_states[-1]

						assert torch.allclose(__snake_case						,    __snake_case		), "The model logits don't match the original one."

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / name						,    commit_message='''Add model'''						,    use_temp_dir=__snake_case						,    )

												SCREAMING_SNAKE_CASE				:       List[Any]     							=				224 if 'seer' not in name else 384
												# we can use the convnext one
												SCREAMING_SNAKE_CASE				:       List[str]     							=				AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''						,    size=__snake_case		)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / name						,    commit_message='''Add image processor'''						,    use_temp_dir=__snake_case						,    )

												print(f"""Pushed {name}"""		)
def  A      (       _lowercase						,    _lowercase = None						,    _lowercase = True		):
						SCREAMING_SNAKE_CASE				:       List[Any]     							=				'imagenet-1k-id2label.json'
						SCREAMING_SNAKE_CASE				:       int     							=				1_000
						SCREAMING_SNAKE_CASE				:       int     							=				(1, num_labels)

						SCREAMING_SNAKE_CASE				:       Dict     							=				'huggingface/label-files'
						SCREAMING_SNAKE_CASE				:       Dict     							=				num_labels
						SCREAMING_SNAKE_CASE				:       List[Any]     							=				json.load(open(cached_download(hf_hub_url(__snake_case						,    __snake_case						,    repo_type='''dataset'''		)		)						,    '''r'''		)		)
						SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				{int(__snake_case		): v for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				idalabel
						SCREAMING_SNAKE_CASE				:       Tuple     							=				{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				partial(__snake_case						,    num_labels=__snake_case						,    idalabel=__snake_case						,    labelaid=__snake_case		)

						SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				{
						    'regnet-x-002': ImageNetPreTrainedConfig(
						        depths=[1, 1, 4, 7]						,    hidden_sizes=[24, 56, 152, 368]						,    groups_width=8						,    layer_type='''x'''		),
						    'regnet-x-004': ImageNetPreTrainedConfig(
						        depths=[1, 2, 7, 12]						,    hidden_sizes=[32, 64, 160, 384]						,    groups_width=16						,    layer_type='''x'''		),
						    'regnet-x-006': ImageNetPreTrainedConfig(
						        depths=[1, 3, 5, 7]						,    hidden_sizes=[48, 96, 240, 528]						,    groups_width=24						,    layer_type='''x'''		),
						    'regnet-x-008': ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 5]						,    hidden_sizes=[64, 128, 288, 672]						,    groups_width=16						,    layer_type='''x'''		),
						    'regnet-x-016': ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 2]						,    hidden_sizes=[72, 168, 408, 912]						,    groups_width=24						,    layer_type='''x'''		),
						    'regnet-x-032': ImageNetPreTrainedConfig(
						        depths=[2, 6, 15, 2]						,    hidden_sizes=[96, 192, 432, 1_008]						,    groups_width=48						,    layer_type='''x'''		),
						    'regnet-x-040': ImageNetPreTrainedConfig(
						        depths=[2, 5, 14, 2]						,    hidden_sizes=[80, 240, 560, 1_360]						,    groups_width=40						,    layer_type='''x'''		),
						    'regnet-x-064': ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]						,    hidden_sizes=[168, 392, 784, 1_624]						,    groups_width=56						,    layer_type='''x'''		),
						    'regnet-x-080': ImageNetPreTrainedConfig(
						        depths=[2, 5, 15, 1]						,    hidden_sizes=[80, 240, 720, 1_920]						,    groups_width=120						,    layer_type='''x'''		),
						    'regnet-x-120': ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]						,    hidden_sizes=[224, 448, 896, 2_240]						,    groups_width=112						,    layer_type='''x'''		),
						    'regnet-x-160': ImageNetPreTrainedConfig(
						        depths=[2, 6, 13, 1]						,    hidden_sizes=[256, 512, 896, 2_048]						,    groups_width=128						,    layer_type='''x'''		),
						    'regnet-x-320': ImageNetPreTrainedConfig(
						        depths=[2, 7, 13, 1]						,    hidden_sizes=[336, 672, 1_344, 2_520]						,    groups_width=168						,    layer_type='''x'''		),
						    # y variant
						    'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]						,    hidden_sizes=[24, 56, 152, 368]						,    groups_width=8		),
						    'regnet-y-004': ImageNetPreTrainedConfig(
						        depths=[1, 3, 6, 6]						,    hidden_sizes=[48, 104, 208, 440]						,    groups_width=8		),
						    'regnet-y-006': ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 4]						,    hidden_sizes=[48, 112, 256, 608]						,    groups_width=16		),
						    'regnet-y-008': ImageNetPreTrainedConfig(
						        depths=[1, 3, 8, 2]						,    hidden_sizes=[64, 128, 320, 768]						,    groups_width=16		),
						    'regnet-y-016': ImageNetPreTrainedConfig(
						        depths=[2, 6, 17, 2]						,    hidden_sizes=[48, 120, 336, 888]						,    groups_width=24		),
						    'regnet-y-032': ImageNetPreTrainedConfig(
						        depths=[2, 5, 13, 1]						,    hidden_sizes=[72, 216, 576, 1_512]						,    groups_width=24		),
						    'regnet-y-040': ImageNetPreTrainedConfig(
						        depths=[2, 6, 12, 2]						,    hidden_sizes=[128, 192, 512, 1_088]						,    groups_width=64		),
						    'regnet-y-064': ImageNetPreTrainedConfig(
						        depths=[2, 7, 14, 2]						,    hidden_sizes=[144, 288, 576, 1_296]						,    groups_width=72		),
						    'regnet-y-080': ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]						,    hidden_sizes=[168, 448, 896, 2_016]						,    groups_width=56		),
						    'regnet-y-120': ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]						,    hidden_sizes=[224, 448, 896, 2_240]						,    groups_width=112		),
						    'regnet-y-160': ImageNetPreTrainedConfig(
						        depths=[2, 4, 11, 1]						,    hidden_sizes=[224, 448, 1_232, 3_024]						,    groups_width=112		),
						    'regnet-y-320': ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]						,    hidden_sizes=[232, 696, 1_392, 3_712]						,    groups_width=232		),
						    # models created by SEER -> https://arxiv.org/abs/2202.08360
						    'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1]						,    hidden_sizes=[232, 696, 1_392, 3_712]						,    groups_width=232		),
						    'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]						,    hidden_sizes=[328, 984, 1_968, 4_920]						,    groups_width=328		),
						    'regnet-y-1280-seer': RegNetConfig(
						        depths=[2, 7, 17, 1]						,    hidden_sizes=[528, 1_056, 2_904, 7_392]						,    groups_width=264		),
						    'regnet-y-2560-seer': RegNetConfig(
						        depths=[3, 7, 16, 1]						,    hidden_sizes=[640, 1_696, 2_544, 5_088]						,    groups_width=640		),
						    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]						,    hidden_sizes=[2_020, 4_040, 11_110, 28_280]						,    groups_width=1_010		),
						    # finetuned on imagenet
						    'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]						,    hidden_sizes=[232, 696, 1_392, 3_712]						,    groups_width=232		),
						    'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]						,    hidden_sizes=[328, 984, 1_968, 4_920]						,    groups_width=328		),
						    'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]						,    hidden_sizes=[528, 1_056, 2_904, 7_392]						,    groups_width=264		),
						    'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[3, 7, 16, 1]						,    hidden_sizes=[640, 1_696, 2_544, 5_088]						,    groups_width=640		),
						    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]						,    hidden_sizes=[2_020, 4_040, 11_110, 28_280]						,    groups_width=1_010		),
						}

						SCREAMING_SNAKE_CASE				:       List[Any]     							=				NameToOurModelFuncMap()
						SCREAMING_SNAKE_CASE				:       List[str]     							=				NameToFromModelFuncMap()
						# add seer weights logic

						def load_using_classy_vision(_lowercase						,    _lowercase		) -> Tuple[nn.Module, Dict]:
												SCREAMING_SNAKE_CASE				:       Tuple     							=				torch.hub.load_state_dict_from_url(__snake_case						,    model_dir=str(__snake_case		)						,    map_location='''cpu'''		)
												SCREAMING_SNAKE_CASE				:       Dict     							=				model_func()
												# check if we have a head, if yes add it
												SCREAMING_SNAKE_CASE				:       List[Any]     							=				files['classy_state_dict']['base_model']['model']
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				model_state_dict['trunk']
												model.load_state_dict(__snake_case		)
												return model.eval(), model_state_dict["heads"]

						# pretrained
						SCREAMING_SNAKE_CASE				:       int     							=				partial(
						    __snake_case						,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''						,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)						,    )

						SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				partial(
						    __snake_case						,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''						,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)						,    )

						SCREAMING_SNAKE_CASE				:       int     							=				partial(
						    __snake_case						,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'''						,    lambda: FakeRegNetVisslWrapper(RegNetYaaagf()		)						,    )

						SCREAMING_SNAKE_CASE				:       Any     							=				partial(
						    __snake_case						,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''						,    lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=27						,    group_width=1_010						,    w_a=1_744						,    w_a=620.83						,    w_m=2.52		)		)		)						,    )

						# IN1K finetuned
						SCREAMING_SNAKE_CASE				:       Dict     							=				partial(
						    __snake_case						,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''						,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)						,    )

						SCREAMING_SNAKE_CASE				:       List[str]     							=				partial(
						    __snake_case						,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''						,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)						,    )

						SCREAMING_SNAKE_CASE				:       Optional[int]     							=				partial(
						    __snake_case						,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''						,    lambda: FakeRegNetVisslWrapper(RegNetYaaagf()		)						,    )

						SCREAMING_SNAKE_CASE				:       int     							=				partial(
						    __snake_case						,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''						,    lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=27						,    group_width=1_010						,    w_a=1_744						,    w_a=620.83						,    w_m=2.52		)		)		)						,    )

						if model_name:
												convert_weight_and_push(
												    __snake_case						,    names_to_from_model_map[model_name]						,    names_to_ours_model_map[model_name]						,    names_to_config[model_name]						,    __snake_case						,    __snake_case						,    )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    __snake_case						,    names_to_from_model_map[model_name]						,    names_to_ours_model_map[model_name]						,    __snake_case						,    __snake_case						,    __snake_case						,    )
						return config, expected_shape


if __name__ == "__main__":
					__UpperCamelCase						:			Tuple     		=	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default=None,
					    type=str,
					    help=(
					        'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
					        ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
					    ),
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path',
					    default=None,
					    type=Path,
					    required=True,
					    help='Path to the output PyTorch model directory.',
					)
					parser.add_argument(
					    '--push_to_hub',
					    default=True,
					    type=bool,
					    required=False,
					    help='If True, push model and image processor to the hub.',
					)

					__UpperCamelCase						:			Optional[int]     		=	parser.parse_args()

					__UpperCamelCase						:			Path     		=	args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)












'''simple docstring'''




import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


lowercase__       : Any         =       '''hf-internal-testing/tiny-random-bert'''
lowercase__       : Optional[Any]         =       os.path.join(TRANSFORMERS_CACHE, '''models--hf-internal-testing--tiny-random-bert''')
lowercase__       : List[Any]         =       '''9b8c223d42b2188cb49d29af482996f9d0f3e5a6'''


class 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			cached_file(_UpperCAmelCase		,      _UpperCAmelCase)
          # Should have downloaded the file in here
          self.assertTrue(os.path.isdir(_UpperCAmelCase))
          # Cache should contain at least those three subfolders:
          for subfolder in ["blobs", "refs", "snapshots"]:
               self.assertTrue(os.path.isdir(os.path.join(_UpperCAmelCase		,      _UpperCAmelCase)))
          with open(os.path.join(_UpperCAmelCase		,      'refs'		,      'main')) as f:
               __A		:					Any						=			f.read()
          self.assertEqual(_UpperCAmelCase		,      os.path.join(_UpperCAmelCase		,      'snapshots'		,      _UpperCAmelCase		,      _UpperCAmelCase))
          self.assertTrue(os.path.isfile(_UpperCAmelCase))

          # File is cached at the same place the second time.
          __A		:					Tuple						=			cached_file(_UpperCAmelCase		,      _UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase		,      _UpperCAmelCase)

          # Using a specific revision to test the full commit hash.
          __A		:					List[Any]						=			cached_file(_UpperCAmelCase		,      _UpperCAmelCase		,      revision='9b8c223')
          self.assertEqual(_UpperCAmelCase		,      os.path.join(_UpperCAmelCase		,      'snapshots'		,      _UpperCAmelCase		,      _UpperCAmelCase))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          with self.assertRaisesRegex(_UpperCAmelCase		,      'is not a valid model identifier'):
               __A		:					Dict						=			cached_file('tiny-random-bert'		,      _UpperCAmelCase)

          with self.assertRaisesRegex(_UpperCAmelCase		,      'is not a valid git identifier'):
               __A		:					Optional[int]						=			cached_file(_UpperCAmelCase		,      _UpperCAmelCase		,      revision='aaaa')

          with self.assertRaisesRegex(_UpperCAmelCase		,      'does not appear to have a file named'):
               __A		:					int						=			cached_file(_UpperCAmelCase		,      'conf')


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          with self.assertRaisesRegex(_UpperCAmelCase		,      'does not appear to have a file named'):
               __A		:					Any						=			cached_file(_UpperCAmelCase		,      'conf')

          with open(os.path.join(_UpperCAmelCase		,      'refs'		,      'main')) as f:
               __A		:					Dict						=			f.read()
          self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase		,      '.no_exist'		,      _UpperCAmelCase		,      'conf')))

          __A		:					List[Any]						=			cached_file(_UpperCAmelCase		,      'conf'		,      _raise_exceptions_for_missing_entries=_UpperCAmelCase)
          self.assertIsNone(_UpperCAmelCase)

          __A		:					str						=			cached_file(_UpperCAmelCase		,      'conf'		,      local_files_only=_UpperCAmelCase		,      _raise_exceptions_for_missing_entries=_UpperCAmelCase)
          self.assertIsNone(_UpperCAmelCase)

          __A		:					List[str]						=			mock.Mock()
          __A		:					Dict						=			500
          __A		:					List[str]						=			{}
          __A		:					List[Any]						=			HTTPError
          __A		:					Optional[Any]						=			{}

          # Under the mock environment we get a 500 error when trying to reach the tokenizer.
          with mock.patch('requests.Session.request'		,      return_value=_UpperCAmelCase) as mock_head:
               __A		:					Dict						=			cached_file(_UpperCAmelCase		,      'conf'		,      _raise_exceptions_for_connection_errors=_UpperCAmelCase)
               self.assertIsNone(_UpperCAmelCase)
               # This check we did call the fake head request
               mock_head.assert_called()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          self.assertTrue(has_file('hf-internal-testing/tiny-bert-pt-only'		,      _UpperCAmelCase))
          self.assertFalse(has_file('hf-internal-testing/tiny-bert-pt-only'		,      _UpperCAmelCase))
          self.assertFalse(has_file('hf-internal-testing/tiny-bert-pt-only'		,      _UpperCAmelCase))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          self.assertIsNone(get_file_from_repo('bert-base-cased'		,      'ahah.txt'))

          # The function raises if the repository does not exist.
          with self.assertRaisesRegex(_UpperCAmelCase		,      'is not a valid model identifier'):
               get_file_from_repo('bert-base-case'		,      _UpperCAmelCase)

          # The function raises if the revision does not exist.
          with self.assertRaisesRegex(_UpperCAmelCase		,      'is not a valid git identifier'):
               get_file_from_repo('bert-base-cased'		,      _UpperCAmelCase		,      revision='ahaha')

          __A		:					List[str]						=			get_file_from_repo('bert-base-cased'		,      _UpperCAmelCase)
          # The name is the cached name which is not very easy to test, so instead we load the content.
          __A		:					List[str]						=			json.loads(open(_UpperCAmelCase		,      'r').read())
          self.assertEqual(config['hidden_size']		,      768)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          with tempfile.TemporaryDirectory() as tmp_dir:
               __A		:					Tuple						=			Path(_UpperCAmelCase) / 'a.txt'
               filename.touch()
               self.assertEqual(get_file_from_repo(_UpperCAmelCase		,      'a.txt')		,      str(_UpperCAmelCase))

               self.assertIsNone(get_file_from_repo(_UpperCAmelCase		,      'b.txt'))