
"""simple docstring"""
import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


lowercase		:    str			=       [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-generation",
        "text-classification",
        "token-classification",
        "language-modeling",
        "multiple-choice",
        "question-answering",
        "summarization",
        "translation",
        "image-classification",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
			import run_clm
			import run_image_classification
			import run_mlm
			import run_ner
			import run_qa as run_squad
			import run_summarization
			import run_swag
			import run_text_classification
			import run_translation


logging.basicConfig(level=logging.DEBUG)

lowercase		:    Optional[int]			=       logging.getLogger()


def      lowerCamelCase_     (						)  ->      Optional[int]:


					'''simple docstring'''
					__a:    Tuple		     =       argparse.ArgumentParser()
					parser.add_argument("""-f"""			)
					__a:    Union[str, Any]		     =       parser.parse_args()
					return args.f


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      Optional[int]:


					'''simple docstring'''
					__a:    Tuple		     =       {}
					__a:    Any		     =       os.path.join(_lowerCamelCase						,					"""all_results.json"""			)
					if os.path.exists(_lowerCamelCase			):
										with open(_lowerCamelCase						,					"""r"""			) as f:
															__a:    List[Any]		     =       json.load(_lowerCamelCase			)
					else:
										raise ValueError(f"""can\'t find {path}"""			)
					return results


def      lowerCamelCase_     (						)  ->      List[Any]:


					'''simple docstring'''
					return bool(tf.config.list_physical_devices("""GPU"""			)			)


lowercase		:    Optional[int]			=       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class  _snake_case	(    snake_case_							):






							@skip("""Skipping until shape inference for to_tf_dataset PR is merged."""     )
							def 				__lowercase	(       self    :      Union[str, Any]     ):






												"""simple docstring"""



												__a:    int		     =       self.get_auto_remove_tmp_dir()
												__a:    int		     =       f"""
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

												if is_cuda_available():
																	testargs.append("""--fp16"""     )

												with patch.object(lowerCamelCase_    ,			"""argv"""    ,			lowerCamelCase_     ):
																	run_text_classification.main()
																	# Reset the mixed precision policy so we don't break other tests
																	tf.keras.mixed_precision.set_global_policy("""float32"""     )
																	__a:    Optional[Any]		     =       get_results(lowerCamelCase_     )
																	self.assertGreaterEqual(result["""eval_accuracy"""]    ,			0.75     )






							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												__a:    Union[str, Any]		     =       self.get_auto_remove_tmp_dir()
												__a:    List[Any]		     =       f"""
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

												if len(tf.config.list_physical_devices("""GPU"""     )     ) > 1:
																	# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																	return

												with patch.object(lowerCamelCase_    ,			"""argv"""    ,			lowerCamelCase_     ):
																	run_clm.main()
																	__a:    Optional[Any]		     =       get_results(lowerCamelCase_     )
																	self.assertLess(result["""eval_perplexity"""]    ,			1_0_0     )






							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												__a:    Optional[Any]		     =       self.get_auto_remove_tmp_dir()
												__a:    Optional[Any]		     =       f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        """.split()

												with patch.object(lowerCamelCase_    ,			"""argv"""    ,			lowerCamelCase_     ):
																	run_mlm.main()
																	__a:    int		     =       get_results(lowerCamelCase_     )
																	self.assertLess(result["""eval_perplexity"""]    ,			4_2     )






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    Any		     =       7 if get_gpu_count() > 1 else 2

												__a:    int		     =       self.get_auto_remove_tmp_dir()
												__a:    Tuple		     =       f"""
            run_ner.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}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

												with patch.object(lowerCamelCase_    ,			"""argv"""    ,			lowerCamelCase_     ):
																	run_ner.main()
																	__a:    Optional[Any]		     =       get_results(lowerCamelCase_     )
																	self.assertGreaterEqual(result["""accuracy"""]    ,			0.75     )






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    str		     =       self.get_auto_remove_tmp_dir()
												__a:    Union[str, Any]		     =       f"""
            run_qa.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}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

												with patch.object(lowerCamelCase_    ,			"""argv"""    ,			lowerCamelCase_     ):
																	run_squad.main()
																	__a:    Any		     =       get_results(lowerCamelCase_     )
																	self.assertGreaterEqual(result["""f1"""]    ,			3_0     )
																	self.assertGreaterEqual(result["""exact"""]    ,			3_0     )






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												__a:    Dict		     =       self.get_auto_remove_tmp_dir()
												__a:    Union[str, Any]		     =       f"""
            run_swag.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}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

												with patch.object(lowerCamelCase_    ,			"""argv"""    ,			lowerCamelCase_     ):
																	run_swag.main()
																	__a:    List[str]		     =       get_results(lowerCamelCase_     )
																	self.assertGreaterEqual(result["""val_accuracy"""]    ,			0.8     )






							@slow
							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    int		     =       self.get_auto_remove_tmp_dir()
												__a:    Optional[Any]		     =       f"""
            run_summarization.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}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

												with patch.object(lowerCamelCase_    ,			"""argv"""    ,			lowerCamelCase_     ):
																	run_summarization.main()
																	__a:    Dict		     =       get_results(lowerCamelCase_     )
																	self.assertGreaterEqual(result["""rouge1"""]    ,			1_0     )
																	self.assertGreaterEqual(result["""rouge2"""]    ,			2     )
																	self.assertGreaterEqual(result["""rougeL"""]    ,			7     )
																	self.assertGreaterEqual(result["""rougeLsum"""]    ,			7     )






							@slow
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												__a:    List[str]		     =       self.get_auto_remove_tmp_dir()
												__a:    Union[str, Any]		     =       f"""
            run_translation.py
            --model_name_or_path Rocketknight1/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}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        """.split()

												with patch.object(lowerCamelCase_    ,			"""argv"""    ,			lowerCamelCase_     ):
																	run_translation.main()
																	__a:    int		     =       get_results(lowerCamelCase_     )
																	self.assertGreaterEqual(result["""bleu"""]    ,			3_0     )






							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    Any		     =       self.get_auto_remove_tmp_dir()
												__a:    Optional[int]		     =       f"""
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            """.split()

												with patch.object(lowerCamelCase_    ,			"""argv"""    ,			lowerCamelCase_     ):
																	run_image_classification.main()
																	__a:    Dict		     =       get_results(lowerCamelCase_     )
																	self.assertGreaterEqual(result["""accuracy"""]    ,			0.7     )








"""simple docstring"""
import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 						a_ (      snake_case_    ,		unittest.TestCase					):







      '''simple docstring'''

      lowerCamelCase__			:							Optional[Any]					=      KandinskyPriorPipeline
      lowerCamelCase__			:							Any					=      ['prompt']
      lowerCamelCase__			:							Any					=      ['prompt', 'negative_prompt']
      lowerCamelCase__			:							str					=      [
          'num_images_per_prompt',
          'generator',
          'num_inference_steps',
          'latents',
          'negative_prompt',
          'guidance_scale',
          'output_type',
          'return_dict',
      ]
      lowerCamelCase__			:							Dict					=      False




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return 3_2




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return 3_2




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return self.time_input_dim




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return self.time_input_dim * 4




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return 1_0_0




      @property
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )
       return tokenizer




      @property
      def      a__				(self   ):

       '''simple docstring'''



       torch.manual_seed(0   )
       lowerCamelCase__	:     int								=		CLIPTextConfig(
           bos_token_id=0,	eos_token_id=2,	hidden_size=self.text_embedder_hidden_size,	projection_dim=self.text_embedder_hidden_size,	intermediate_size=3_7,	layer_norm_eps=1e-05,	num_attention_heads=4,	num_hidden_layers=5,	pad_token_id=1,	vocab_size=1_0_0_0,	)
       return CLIPTextModelWithProjection(lowerCamelCase_   )




      @property
      def      a__				(self   ):

       '''simple docstring'''



       torch.manual_seed(0   )

       lowerCamelCase__	:     Optional[int]								=		{
           'num_attention_heads': 2,
           'attention_head_dim': 1_2,
           'embedding_dim': self.text_embedder_hidden_size,
           'num_layers': 1,
       }

       lowerCamelCase__	:     Optional[int]								=		PriorTransformer(**lowerCamelCase_   )
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       lowerCamelCase__	:     Dict								=		nn.Parameter(torch.ones(model.clip_std.shape   )   )
       return model




      @property
      def      a__				(self   ):

       '''simple docstring'''



       torch.manual_seed(0   )
       lowerCamelCase__	:     List[str]								=		CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size,	image_size=2_2_4,	projection_dim=self.text_embedder_hidden_size,	intermediate_size=3_7,	num_attention_heads=4,	num_channels=3,	num_hidden_layers=5,	patch_size=1_4,	)

       lowerCamelCase__	:     int								=		CLIPVisionModelWithProjection(lowerCamelCase_   )
       return model




      @property
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Union[str, Any]								=		CLIPImageProcessor(
           crop_size=2_2_4,	do_center_crop=lowerCamelCase_,	do_normalize=lowerCamelCase_,	do_resize=lowerCamelCase_,	image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073],	image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711],	resample=3,	size=2_2_4,	)

       return image_processor




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		self.dummy_prior
       lowerCamelCase__	:     Optional[Any]								=		self.dummy_image_encoder
       lowerCamelCase__	:     Optional[Any]								=		self.dummy_text_encoder
       lowerCamelCase__	:     Union[str, Any]								=		self.dummy_tokenizer
       lowerCamelCase__	:     List[Any]								=		self.dummy_image_processor

       lowerCamelCase__	:     List[Any]								=		UnCLIPScheduler(
           variance_type='fixed_small_log',	prediction_type='sample',	num_train_timesteps=1_0_0_0,	clip_sample=lowerCamelCase_,	clip_sample_range=10.0,	)

       lowerCamelCase__	:     Union[str, Any]								=		{
           'prior': prior,
           'image_encoder': image_encoder,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'scheduler': scheduler,
           'image_processor': image_processor,
       }

       return components




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_=0   ):

       '''simple docstring'''



       if str(lowerCamelCase_   ).startswith('mps'   ):
        lowerCamelCase__	:     List[Any]								=		torch.manual_seed(lowerCamelCase_   )
       else:
        lowerCamelCase__	:     Optional[int]								=		torch.Generator(device=lowerCamelCase_   ).manual_seed(lowerCamelCase_   )
       lowerCamelCase__	:     List[Any]								=		{
           'prompt': 'horse',
           'generator': generator,
           'guidance_scale': 4.0,
           'num_inference_steps': 2,
           'output_type': 'np',
       }
       return inputs




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[str]								=		'cpu'

       lowerCamelCase__	:     str								=		self.get_dummy_components()

       lowerCamelCase__	:     Any								=		self.pipeline_class(**lowerCamelCase_   )
       lowerCamelCase__	:     Dict								=		pipe.to(lowerCamelCase_   )

       pipe.set_progress_bar_config(disable=lowerCamelCase_   )

       lowerCamelCase__	:     Tuple								=		pipe(**self.get_dummy_inputs(lowerCamelCase_   )   )
       lowerCamelCase__	:     Dict								=		output.image_embeds

       lowerCamelCase__	:     Optional[Any]								=		pipe(
           **self.get_dummy_inputs(lowerCamelCase_   ),	return_dict=lowerCamelCase_,	)[0]

       lowerCamelCase__	:     Dict								=		image[0, -1_0:]
       lowerCamelCase__	:     Tuple								=		image_from_tuple[0, -1_0:]

       assert image.shape == (1, 3_2)

       lowerCamelCase__	:     Dict								=		np.array(
           [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2




      @skip_mps
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		torch_device == 'cpu'
       lowerCamelCase__	:     Any								=		True
       lowerCamelCase__	:     List[Any]								=		False

       self._test_inference_batch_single_identical(
           test_max_difference=lowerCamelCase_,	relax_max_difference=lowerCamelCase_,	test_mean_pixel_difference=lowerCamelCase_,	)





      @skip_mps
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		torch_device == 'cpu'
       lowerCamelCase__	:     int								=		False

       self._test_attention_slicing_forward_pass(
           test_max_difference=lowerCamelCase_,	test_mean_pixel_difference=lowerCamelCase_,	)

