





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     :			Optional[int]     						=	[
    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     :			List[Any]     						=	logging.getLogger()


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    argparse.ArgumentParser()
       parser.add_argument('''-f'''       )
       _UpperCamelCase														=    parser.parse_args()
       return args.f


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       _UpperCamelCase														=    {}
       _UpperCamelCase														=    os.path.join(lowerCAmelCase     ,							'''all_results.json'''       )
       if os.path.exists(lowerCAmelCase       ):
              with open(lowerCAmelCase     ,							'''r'''       ) as f:
                     _UpperCamelCase														=    json.load(lowerCAmelCase       )
       else:
              raise ValueError(f'''can\'t find {path}'''       )
       return results


def      SCREAMING_SNAKE_CASE      (   ):
       return bool(tf.config.list_physical_devices('''GPU'''       )       )


lowercase     :			Dict     						=	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class 			__A(    lowerCAmelCase_			):

     @skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''   )
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

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

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_auto_remove_tmp_dir()
            _UpperCamelCase														=    F'''\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '''.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(__UpperCamelCase,							'''argv''',							__UpperCamelCase   ):
                   run_clm.main()
                   _UpperCamelCase														=    get_results(__UpperCamelCase   )
                   self.assertLess(result['''eval_perplexity'''],							100   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_auto_remove_tmp_dir()
            _UpperCamelCase														=    F'''\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        '''.split()

            with patch.object(__UpperCamelCase,							'''argv''',							__UpperCamelCase   ):
                   run_mlm.main()
                   _UpperCamelCase														=    get_results(__UpperCamelCase   )
                   self.assertLess(result['''eval_perplexity'''],							42   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    7 if get_gpu_count() > 1 else 2

            _UpperCamelCase														=    self.get_auto_remove_tmp_dir()
            _UpperCamelCase														=    F'''\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '''.split()

            with patch.object(__UpperCamelCase,							'''argv''',							__UpperCamelCase   ):
                   run_ner.main()
                   _UpperCamelCase														=    get_results(__UpperCamelCase   )
                   self.assertGreaterEqual(result['''accuracy'''],							0.75   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_auto_remove_tmp_dir()
            _UpperCamelCase														=    F'''\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '''.split()

            with patch.object(__UpperCamelCase,							'''argv''',							__UpperCamelCase   ):
                   run_squad.main()
                   _UpperCamelCase														=    get_results(__UpperCamelCase   )
                   self.assertGreaterEqual(result['''f1'''],							30   )
                   self.assertGreaterEqual(result['''exact'''],							30   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_auto_remove_tmp_dir()
            _UpperCamelCase														=    F'''\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '''.split()

            with patch.object(__UpperCamelCase,							'''argv''',							__UpperCamelCase   ):
                   run_swag.main()
                   _UpperCamelCase														=    get_results(__UpperCamelCase   )
                   self.assertGreaterEqual(result['''val_accuracy'''],							0.8   )

     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_auto_remove_tmp_dir()
            _UpperCamelCase														=    F'''\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '''.split()

            with patch.object(__UpperCamelCase,							'''argv''',							__UpperCamelCase   ):
                   run_summarization.main()
                   _UpperCamelCase														=    get_results(__UpperCamelCase   )
                   self.assertGreaterEqual(result['''rouge1'''],							10   )
                   self.assertGreaterEqual(result['''rouge2'''],							2   )
                   self.assertGreaterEqual(result['''rougeL'''],							7   )
                   self.assertGreaterEqual(result['''rougeLsum'''],							7   )

     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_auto_remove_tmp_dir()
            _UpperCamelCase														=    F'''\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        '''.split()

            with patch.object(__UpperCamelCase,							'''argv''',							__UpperCamelCase   ):
                   run_translation.main()
                   _UpperCamelCase														=    get_results(__UpperCamelCase   )
                   self.assertGreaterEqual(result['''bleu'''],							30   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_auto_remove_tmp_dir()
            _UpperCamelCase														=    F'''\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            '''.split()

            with patch.object(__UpperCamelCase,							'''argv''',							__UpperCamelCase   ):
                   run_image_classification.main()
                   _UpperCamelCase														=    get_results(__UpperCamelCase   )
                   self.assertGreaterEqual(result['''accuracy'''],							0.7   )








"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block







@dataclass
class 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     _A		    =				42







class 	_lowercase    (							lowerCAmelCase_						, lowerCAmelCase_					):


     '''simple docstring'''





     @register_to_config
     def __init__(   self			,      __UpperCamelCase = None			,      __UpperCamelCase = 3			,      __UpperCamelCase = 3			,      __UpperCamelCase = False			,      __UpperCamelCase = "positional"			,      __UpperCamelCase = 0			,      __UpperCamelCase = True			,      __UpperCamelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")			,      __UpperCamelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")			,      __UpperCamelCase = (2_24, 4_48, 6_72, 8_96)			,      __UpperCamelCase = 2			,      __UpperCamelCase = 1			,      __UpperCamelCase = 1			,      __UpperCamelCase = "conv"			,      __UpperCamelCase = "conv"			,      __UpperCamelCase = "silu"			,      __UpperCamelCase = 8			,      __UpperCamelCase = 32			,      __UpperCamelCase = 1E-5			,      __UpperCamelCase = "default"			,      __UpperCamelCase = True			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      )->       Any:
          super().__init__()

          UpperCAmelCase__				:			Optional[int]							=    sample_size
          UpperCAmelCase__				:			Any							=    block_out_channels[0] * 4

          # Check inputs
          if len(__UpperCamelCase	) != len(__UpperCamelCase	):
               raise ValueError(
                   F"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."	)

          if len(__UpperCamelCase	) != len(__UpperCamelCase	):
               raise ValueError(
                   F"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."	)

          # input
          UpperCAmelCase__				:			List[Any]							=    nn.Convad(__UpperCamelCase			,      block_out_channels[0]			,      kernel_size=3			,      padding=(1, 1)	)

          # time
          if time_embedding_type == "fourier":
               UpperCAmelCase__				:			int							=    GaussianFourierProjection(embedding_size=block_out_channels[0]			,      scale=16	)
               UpperCAmelCase__				:			Dict							=    2 * block_out_channels[0]
          elif time_embedding_type == "positional":
               UpperCAmelCase__				:			List[str]							=    Timesteps(block_out_channels[0]			,      __UpperCamelCase			,      __UpperCamelCase	)
               UpperCAmelCase__				:			Any							=    block_out_channels[0]

          UpperCAmelCase__				:			List[str]							=    TimestepEmbedding(__UpperCamelCase			,      __UpperCamelCase	)

          # class embedding
          if class_embed_type is None and num_class_embeds is not None:
               UpperCAmelCase__				:			Optional[Any]							=    nn.Embedding(__UpperCamelCase			,      __UpperCamelCase	)
          elif class_embed_type == "timestep":
               UpperCAmelCase__				:			str							=    TimestepEmbedding(__UpperCamelCase			,      __UpperCamelCase	)
          elif class_embed_type == "identity":
               UpperCAmelCase__				:			Union[str, Any]							=    nn.Identity(__UpperCamelCase			,      __UpperCamelCase	)
          else:
               UpperCAmelCase__				:			Optional[int]							=    None

          UpperCAmelCase__				:			str							=    nn.ModuleList([]	)
          UpperCAmelCase__				:			Any							=    None
          UpperCAmelCase__				:			Tuple							=    nn.ModuleList([]	)

          # down
          UpperCAmelCase__				:			Any							=    block_out_channels[0]
          for i, down_block_type in enumerate(__UpperCamelCase	):
               UpperCAmelCase__				:			List[Any]							=    output_channel
               UpperCAmelCase__				:			Optional[int]							=    block_out_channels[i]
               UpperCAmelCase__				:			Any							=    i == len(__UpperCamelCase	) - 1

               UpperCAmelCase__				:			List[Any]							=    get_down_block(
                   __UpperCamelCase			,      num_layers=__UpperCamelCase			,      in_channels=__UpperCamelCase			,      out_channels=__UpperCamelCase			,      temb_channels=__UpperCamelCase			,      add_downsample=not is_final_block			,      resnet_eps=__UpperCamelCase			,      resnet_act_fn=__UpperCamelCase			,      resnet_groups=__UpperCamelCase			,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel			,      downsample_padding=__UpperCamelCase			,      resnet_time_scale_shift=__UpperCamelCase			,      downsample_type=__UpperCamelCase			,      )
               self.down_blocks.append(__UpperCamelCase	)

          # mid
          UpperCAmelCase__				:			List[str]							=    UNetMidBlockaD(
              in_channels=block_out_channels[-1]			,      temb_channels=__UpperCamelCase			,      resnet_eps=__UpperCamelCase			,      resnet_act_fn=__UpperCamelCase			,      output_scale_factor=__UpperCamelCase			,      resnet_time_scale_shift=__UpperCamelCase			,      attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]			,      resnet_groups=__UpperCamelCase			,      add_attention=__UpperCamelCase			,      )

          # up
          UpperCAmelCase__				:			Tuple							=    list(reversed(__UpperCamelCase	)	)
          UpperCAmelCase__				:			Optional[Any]							=    reversed_block_out_channels[0]
          for i, up_block_type in enumerate(__UpperCamelCase	):
               UpperCAmelCase__				:			int							=    output_channel
               UpperCAmelCase__				:			List[Any]							=    reversed_block_out_channels[i]
               UpperCAmelCase__				:			Dict							=    reversed_block_out_channels[min(i + 1			,      len(__UpperCamelCase	) - 1	)]

               UpperCAmelCase__				:			int							=    i == len(__UpperCamelCase	) - 1

               UpperCAmelCase__				:			Dict							=    get_up_block(
                   __UpperCamelCase			,      num_layers=layers_per_block + 1			,      in_channels=__UpperCamelCase			,      out_channels=__UpperCamelCase			,      prev_output_channel=__UpperCamelCase			,      temb_channels=__UpperCamelCase			,      add_upsample=not is_final_block			,      resnet_eps=__UpperCamelCase			,      resnet_act_fn=__UpperCamelCase			,      resnet_groups=__UpperCamelCase			,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel			,      resnet_time_scale_shift=__UpperCamelCase			,      upsample_type=__UpperCamelCase			,      )
               self.up_blocks.append(__UpperCamelCase	)
               UpperCAmelCase__				:			Dict							=    output_channel

          # out
          UpperCAmelCase__				:			str							=    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,      32	)
          UpperCAmelCase__				:			Tuple							=    nn.GroupNorm(num_channels=block_out_channels[0]			,      num_groups=__UpperCamelCase			,      eps=__UpperCamelCase	)
          UpperCAmelCase__				:			Any							=    nn.SiLU()
          UpperCAmelCase__				:			Optional[int]							=    nn.Convad(block_out_channels[0]			,      __UpperCamelCase			,      kernel_size=3			,      padding=1	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase = None			,      __UpperCamelCase = True			,      )->       Union[UNetaDOutput, Tuple]:
          # 0. center input if necessary
          if self.config.center_input_sample:
               UpperCAmelCase__				:			Any							=    2 * sample - 1.0

          # 1. time
          UpperCAmelCase__				:			Tuple							=    timestep
          if not torch.is_tensor(__UpperCamelCase	):
               UpperCAmelCase__				:			Optional[int]							=    torch.tensor([timesteps]			,      dtype=torch.long			,      device=sample.device	)
          elif torch.is_tensor(__UpperCamelCase	) and len(timesteps.shape	) == 0:
               UpperCAmelCase__				:			List[Any]							=    timesteps[None].to(sample.device	)

          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          UpperCAmelCase__				:			Dict							=    timesteps * torch.ones(sample.shape[0]			,      dtype=timesteps.dtype			,      device=timesteps.device	)

          UpperCAmelCase__				:			List[str]							=    self.time_proj(__UpperCamelCase	)

          # timesteps does not contain any weights and will always return f32 tensors
          # but time_embedding might actually be running in fp16. so we need to cast here.
          # there might be better ways to encapsulate this.
          UpperCAmelCase__				:			Dict							=    t_emb.to(dtype=self.dtype	)
          UpperCAmelCase__				:			Optional[int]							=    self.time_embedding(__UpperCamelCase	)

          if self.class_embedding is not None:
               if class_labels is None:
                    raise ValueError("class_labels should be provided when doing class conditioning"	)

               if self.config.class_embed_type == "timestep":
                    UpperCAmelCase__				:			List[Any]							=    self.time_proj(__UpperCamelCase	)

               UpperCAmelCase__				:			List[str]							=    self.class_embedding(__UpperCamelCase	).to(dtype=self.dtype	)
               UpperCAmelCase__				:			Optional[int]							=    emb + class_emb

          # 2. pre-process
          UpperCAmelCase__				:			List[str]							=    sample
          UpperCAmelCase__				:			str							=    self.conv_in(__UpperCamelCase	)

          # 3. down
          UpperCAmelCase__				:			List[str]							=    (sample,)
          for downsample_block in self.down_blocks:
               if hasattr(__UpperCamelCase			,      "skip_conv"	):
                    UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			Optional[Any]							=    downsample_block(
                        hidden_states=__UpperCamelCase			,      temb=__UpperCamelCase			,      skip_sample=__UpperCamelCase	)
               else:
                    UpperCAmelCase__				,						UpperCAmelCase__				:			Optional[Any]							=    downsample_block(hidden_states=__UpperCamelCase			,      temb=__UpperCamelCase	)

               down_block_res_samples += res_samples

          # 4. mid
          UpperCAmelCase__				:			Union[str, Any]							=    self.mid_block(__UpperCamelCase			,      __UpperCamelCase	)

          # 5. up
          UpperCAmelCase__				:			Dict							=    None
          for upsample_block in self.up_blocks:
               UpperCAmelCase__				:			Union[str, Any]							=    down_block_res_samples[-len(upsample_block.resnets	) :]
               UpperCAmelCase__				:			Optional[Any]							=    down_block_res_samples[: -len(upsample_block.resnets	)]

               if hasattr(__UpperCamelCase			,      "skip_conv"	):
                    UpperCAmelCase__				,						UpperCAmelCase__				:			str							=    upsample_block(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)
               else:
                    UpperCAmelCase__				:			Tuple							=    upsample_block(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)

        # 6. post-process
          UpperCAmelCase__				:			List[Any]							=    self.conv_norm_out(__UpperCamelCase	)
          UpperCAmelCase__				:			Any							=    self.conv_act(__UpperCamelCase	)
          UpperCAmelCase__				:			Optional[Any]							=    self.conv_out(__UpperCamelCase	)

          if skip_sample is not None:
               sample += skip_sample

          if self.config.time_embedding_type == "fourier":
               UpperCAmelCase__				:			str							=    timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]	)))	)
               UpperCAmelCase__				:			int							=    sample / timesteps

          if not return_dict:
               return (sample,)

          return UNetaDOutput(sample=__UpperCamelCase	)



