

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							List[str]=1					)	->					str:




				'''simple docstring'''





				if n_shave_prefix_segments >= 0:
								return ".".join(path.split("."					)[n_shave_prefix_segments:]					)
				else:
								return ".".join(path.split("."					)[:n_shave_prefix_segments]					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							Any=0					)	->					Dict:




				'''simple docstring'''





				A__						=	[]
				for old_item in old_list:
								A__						=	old_item.replace("in_layers.0"		,				"norm1"					)
								A__						=	new_item.replace("in_layers.2"		,				"conv1"					)

								A__						=	new_item.replace("out_layers.0"		,				"norm2"					)
								A__						=	new_item.replace("out_layers.3"		,				"conv2"					)

								A__						=	new_item.replace("emb_layers.1"		,				"time_emb_proj"					)
								A__						=	new_item.replace("skip_connection"		,				"conv_shortcut"					)

								A__						=	shave_segments(SCREAMING_SNAKE_CASE_		,				n_shave_prefix_segments=SCREAMING_SNAKE_CASE_					)

								mapping.append({"old": old_item, "new": new_item}					)

				return mapping



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							Tuple=0					)	->					Dict:




				'''simple docstring'''





				A__						=	[]
				for old_item in old_list:
								A__						=	old_item

								A__						=	new_item.replace("norm.weight"		,				"group_norm.weight"					)
								A__						=	new_item.replace("norm.bias"		,				"group_norm.bias"					)

								A__						=	new_item.replace("proj_out.weight"		,				"proj_attn.weight"					)
								A__						=	new_item.replace("proj_out.bias"		,				"proj_attn.bias"					)

								A__						=	shave_segments(SCREAMING_SNAKE_CASE_		,				n_shave_prefix_segments=SCREAMING_SNAKE_CASE_					)

								mapping.append({"old": old_item, "new": new_item}					)

				return mapping



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							int=None		,				SCREAMING_SNAKE_CASE_:							Tuple=None		,				SCREAMING_SNAKE_CASE_:							List[str]=None					)	->					int:




				'''simple docstring'''





				assert isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					), "Paths should be a list of dicts containing 'old' and 'new' keys."

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												A__						=	old_checkpoint[path]
												A__						=	old_tensor.shape[0] // 3

												A__						=	(-1, channels) if len(old_tensor.shape					) == 3 else (-1)

												A__						=	old_tensor.shape[0] // config["num_head_channels"] // 3

												A__						=	old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]					)
												A__			, A__			, A__						=	old_tensor.split(channels // num_heads		,				dim=1					)

												A__						=	query.reshape(SCREAMING_SNAKE_CASE_					)
												A__						=	key.reshape(SCREAMING_SNAKE_CASE_					)
												A__						=	value.reshape(SCREAMING_SNAKE_CASE_					)

				for path in paths:
								A__						=	path["new"]

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								# Global renaming happens here
								A__						=	new_path.replace("middle_block.0"		,				"mid_block.resnets.0"					)
								A__						=	new_path.replace("middle_block.1"		,				"mid_block.attentions.0"					)
								A__						=	new_path.replace("middle_block.2"		,				"mid_block.resnets.1"					)

								if additional_replacements is not None:
												for replacement in additional_replacements:
																A__						=	new_path.replace(replacement["old"]		,				replacement["new"]					)

        # proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												A__						=	old_checkpoint[path["old"]][:, :, 0]
								else:
												A__						=	old_checkpoint[path["old"]]



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							str					)	->					Union[str, Any]:




				'''simple docstring'''





				A__						=	{}

				A__						=	checkpoint["time_embed.0.weight"]
				A__						=	checkpoint["time_embed.0.bias"]
				A__						=	checkpoint["time_embed.2.weight"]
				A__						=	checkpoint["time_embed.2.bias"]

				A__						=	checkpoint["input_blocks.0.0.weight"]
				A__						=	checkpoint["input_blocks.0.0.bias"]

				A__						=	checkpoint["out.0.weight"]
				A__						=	checkpoint["out.0.bias"]
				A__						=	checkpoint["out.2.weight"]
				A__						=	checkpoint["out.2.bias"]

				# Retrieves the keys for the input blocks only
				A__						=	len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "input_blocks" in layer}					)
				A__						=	{
				    layer_id: [key for key in checkpoint if F'input_blocks.{layer_id}' in key]
				    for layer_id in range(SCREAMING_SNAKE_CASE_					)
				}

				# Retrieves the keys for the middle blocks only
				A__						=	len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "middle_block" in layer}					)
				A__						=	{
				    layer_id: [key for key in checkpoint if F'middle_block.{layer_id}' in key]
				    for layer_id in range(SCREAMING_SNAKE_CASE_					)
				}

				# Retrieves the keys for the output blocks only
				A__						=	len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "output_blocks" in layer}					)
				A__						=	{
				    layer_id: [key for key in checkpoint if F'output_blocks.{layer_id}' in key]
				    for layer_id in range(SCREAMING_SNAKE_CASE_					)
				}

				for i in range(1		,				SCREAMING_SNAKE_CASE_					):
								A__						=	(i - 1) // (config["num_res_blocks"] + 1)
								A__						=	(i - 1) % (config["num_res_blocks"] + 1)

								A__						=	[key for key in input_blocks[i] if F'input_blocks.{i}.0' in key]
								A__						=	[key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]

								if F'input_blocks.{i}.0.op.weight' in checkpoint:
												A__						=	checkpoint[
												    F'input_blocks.{i}.0.op.weight'
												]
												A__						=	checkpoint[
												    F'input_blocks.{i}.0.op.bias'
												]
												continue

								A__						=	renew_resnet_paths(SCREAMING_SNAKE_CASE_					)
								A__						=	{"old": F'input_blocks.{i}.0', "new": F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
								A__						=	{"old": "resnets.2.op", "new": "downsamplers.0.op"}
								assign_to_checkpoint(
								    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				additional_replacements=[meta_path, resnet_op]		,				config=SCREAMING_SNAKE_CASE_					)

								if len(SCREAMING_SNAKE_CASE_					):
												A__						=	renew_attention_paths(SCREAMING_SNAKE_CASE_					)
												A__						=	{
												    "old": F'input_blocks.{i}.1',
												    "new": F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
												}
												A__						=	{
												    F'input_blocks.{i}.1.qkv.bias': {
												        "key": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
												        "query": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
												        "value": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
												    },
												    F'input_blocks.{i}.1.qkv.weight': {
												        "key": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
												        "query": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
												        "value": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
												    },
												}
												assign_to_checkpoint(
												    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				additional_replacements=[meta_path]		,				attention_paths_to_split=SCREAMING_SNAKE_CASE_		,				config=SCREAMING_SNAKE_CASE_		,				)

				A__						=	middle_blocks[0]
				A__						=	middle_blocks[1]
				A__						=	middle_blocks[2]

				A__						=	renew_resnet_paths(SCREAMING_SNAKE_CASE_					)
				assign_to_checkpoint(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				config=SCREAMING_SNAKE_CASE_					)

				A__						=	renew_resnet_paths(SCREAMING_SNAKE_CASE_					)
				assign_to_checkpoint(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				config=SCREAMING_SNAKE_CASE_					)

				A__						=	renew_attention_paths(SCREAMING_SNAKE_CASE_					)
				A__						=	{
				    "middle_block.1.qkv.bias": {
				        "key": "mid_block.attentions.0.key.bias",
				        "query": "mid_block.attentions.0.query.bias",
				        "value": "mid_block.attentions.0.value.bias",
				    },
				    "middle_block.1.qkv.weight": {
				        "key": "mid_block.attentions.0.key.weight",
				        "query": "mid_block.attentions.0.query.weight",
				        "value": "mid_block.attentions.0.value.weight",
				    },
				}
				assign_to_checkpoint(
				    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				attention_paths_to_split=SCREAMING_SNAKE_CASE_		,				config=SCREAMING_SNAKE_CASE_					)

				for i in range(SCREAMING_SNAKE_CASE_					):
								A__						=	i // (config["num_res_blocks"] + 1)
								A__						=	i % (config["num_res_blocks"] + 1)
								A__						=	[shave_segments(SCREAMING_SNAKE_CASE_		,				2					) for name in output_blocks[i]]
								A__						=	{}

								for layer in output_block_layers:
												A__			, A__						=	layer.split("."					)[0], shave_segments(SCREAMING_SNAKE_CASE_		,				1					)
												if layer_id in output_block_list:
																output_block_list[layer_id].append(SCREAMING_SNAKE_CASE_					)
												else:
																A__						=	[layer_name]

								if len(SCREAMING_SNAKE_CASE_					) > 1:
												A__						=	[key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
												A__						=	[key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]

												A__						=	renew_resnet_paths(SCREAMING_SNAKE_CASE_					)
												A__						=	renew_resnet_paths(SCREAMING_SNAKE_CASE_					)

												A__						=	{"old": F'output_blocks.{i}.0', "new": F'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
												assign_to_checkpoint(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				additional_replacements=[meta_path]		,				config=SCREAMING_SNAKE_CASE_					)

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																A__						=	list(output_block_list.values()					).index(["conv.weight", "conv.bias"]					)
																A__						=	checkpoint[
																    F'output_blocks.{i}.{index}.conv.weight'
																]
																A__						=	checkpoint[
																    F'output_blocks.{i}.{index}.conv.bias'
																]

																# Clear attentions as they have been attributed above.
																if len(SCREAMING_SNAKE_CASE_					) == 2:
																				A__						=	[]

												if len(SCREAMING_SNAKE_CASE_					):
																A__						=	renew_attention_paths(SCREAMING_SNAKE_CASE_					)
																A__						=	{
																    "old": F'output_blocks.{i}.1',
																    "new": F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
																}
																A__						=	{
																    F'output_blocks.{i}.1.qkv.bias': {
																        "key": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
																        "query": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
																        "value": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
																    },
																    F'output_blocks.{i}.1.qkv.weight': {
																        "key": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
																        "query": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
																        "value": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
																    },
																}
																assign_to_checkpoint(
																    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				additional_replacements=[meta_path]		,				attention_paths_to_split=to_split if any("qkv" in key for key in attentions					) else None		,				config=SCREAMING_SNAKE_CASE_		,				)
								else:
												A__						=	renew_resnet_paths(SCREAMING_SNAKE_CASE_		,				n_shave_prefix_segments=1					)
												for path in resnet_0_paths:
																A__						=	".".join(["output_blocks", str(SCREAMING_SNAKE_CASE_					), path["old"]]					)
																A__						=	".".join(["up_blocks", str(SCREAMING_SNAKE_CASE_					), "resnets", str(SCREAMING_SNAKE_CASE_					), path["new"]]					)

																A__						=	checkpoint[old_path]

				return new_checkpoint


if __name__ == "__main__":
	lowerCAmelCase__		      =       argparse.ArgumentParser()

	parser.add_argument(
	    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
	)

	parser.add_argument(
	    """--config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help="""The config json file corresponding to the architecture.""",
	)

	parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

	lowerCAmelCase__		      =       parser.parse_args()

	lowerCAmelCase__		      =       torch.load(args.checkpoint_path)

	with open(args.config_file) as f:
		lowerCAmelCase__		      =       json.loads(f.read())

	lowerCAmelCase__		      =       convert_ldm_checkpoint(checkpoint, config)

	if "ldm" in config:
		del config["ldm"]

	lowerCAmelCase__		      =       UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		lowerCAmelCase__		      =       DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
		lowerCAmelCase__		      =       VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

		lowerCAmelCase__		      =       LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)









import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase__				   =		logging.getLogger()
def 						_a    (      )							->			Optional[int]:
 a		  =      argparse.ArgumentParser()
 parser.add_argument('''-f''' )
 a		  =      parser.parse_args()
 return args.f
def 						_a    (      a :Any )							->			Tuple:
 a		  =      {}
 a		  =      os.path.join(a	,							'''all_results.json''' )
 if os.path.exists(a ):
  with open(a	,							'''r''' ) as f:
   a		  =      json.load(a )
 else:
  raise ValueError(F"""can't find {path}""" )
 return results
def 						_a    (      )							->			int:
 a		  =      torch.cuda.is_available() and torch_device == '''cuda'''
 return is_using_cuda and is_apex_available()


UpperCAmelCase__				   =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      @classmethod
      def       __lowerCAmelCase					(      cls      :						str  )    ->Tuple:




       """simple docstring"""


       a		  =      tempfile.mkdtemp()
       a		  =      os.path.join(cls.tmpdir ,					'''default_config.yml'''  )
       write_basic_config(save_location=cls.configPath  )
       a		  =      ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]







      @classmethod
      def       __lowerCAmelCase					(      cls      :						Optional[int]  )    ->Union[str, Any]:




       """simple docstring"""


       shutil.rmtree(cls.tmpdir  )







      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       if is_cuda_and_apex_available():
        testargs.append('''--fp16'''  )

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertGreaterEqual(result['''eval_accuracy'''] ,					0.75  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''glue_no_trainer'''  )  )  )







      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Any:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       if torch.cuda.device_count() > 1:
        # Skipping because there are not enough batches to train the model + would need a drop_last to work.
        return

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertLess(result['''perplexity'''] ,					100  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''clm_no_trainer'''  )  )  )







      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->int:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertLess(result['''perplexity'''] ,					42  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''mlm_no_trainer'''  )  )  )







      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->str:




       """simple docstring"""


       a		  =      7 if get_gpu_count() > 1 else 2

       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertGreaterEqual(result['''eval_accuracy'''] ,					0.75  )
       self.assertLess(result['''train_loss'''] ,					0.5  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''ner_no_trainer'''  )  )  )







      @unittest.skip(reason='''Fix me @muellerzr'''  )
      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Any  )    ->int:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
       self.assertGreaterEqual(result['''eval_f1'''] ,					28  )
       self.assertGreaterEqual(result['''eval_exact'''] ,					28  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''qa_no_trainer'''  )  )  )







      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Any:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertGreaterEqual(result['''eval_accuracy'''] ,					0.8  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''swag_no_trainer'''  )  )  )







      @slow
      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertGreaterEqual(result['''eval_rouge1'''] ,					10  )
       self.assertGreaterEqual(result['''eval_rouge2'''] ,					2  )
       self.assertGreaterEqual(result['''eval_rougeL'''] ,					7  )
       self.assertGreaterEqual(result['''eval_rougeLsum'''] ,					7  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''summarization_no_trainer'''  )  )  )







      @slow
      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertGreaterEqual(result['''eval_bleu'''] ,					30  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''epoch_0'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''translation_no_trainer'''  )  )  )







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




       """simple docstring"""


       a		  =      logging.StreamHandler(sys.stdout  )
       logger.addHandler(__UpperCAmelCase  )

       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertGreaterEqual(result['''eval_overall_accuracy'''] ,					0.10  )







      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Tuple:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      F"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

       if is_cuda_and_apex_available():
        testargs.append('''--fp16'''  )

       run_command(self._launch_args + testargs  )
       a		  =      get_results(__UpperCAmelCase  )
       # The base model scores a 25%
       self.assertGreaterEqual(result['''eval_accuracy'''] ,					0.6  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''step_1'''  )  )  )
       self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase ,					'''image_classification_no_trainer'''  )  )  )


