
from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
					import tensorflow as tf

					from transformers import TFBlipTextModel
					from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class      _SCREAMING_SNAKE_CASE				:





				def __init__(		self			:		Dict       ,   snake_case_			:		List[str]       ,   snake_case_			:		Dict=12       ,   snake_case_			:		str=7       ,   snake_case_			:		Any=True       ,   snake_case_			:		List[str]=True       ,   snake_case_			:		Optional[Any]=True       ,   snake_case_			:		Tuple=99       ,   snake_case_			:		int=32       ,   snake_case_			:		Optional[Any]=32       ,   snake_case_			:		Dict=2       ,   snake_case_			:		Any=4       ,   snake_case_			:		str=37       ,   snake_case_			:		List[Any]=0.1       ,   snake_case_			:		List[str]=0.1       ,   snake_case_			:		int=512       ,   snake_case_			:		int=0.02       ,   snake_case_			:		Optional[int]=0       ,   snake_case_			:		Optional[int]=None       ,   ):


								"""simple docstring"""

								A  :	int				=					parent
								A  :	List[str]				=					batch_size
								A  :	Dict				=					seq_length
								A  :	Tuple				=					is_training
								A  :	List[Any]				=					use_input_mask
								A  :	Optional[int]				=					use_labels
								A  :	Optional[int]				=					vocab_size
								A  :	str				=					hidden_size
								A  :	Any				=					projection_dim
								A  :	Dict				=					num_hidden_layers
								A  :	Dict				=					num_attention_heads
								A  :	int				=					intermediate_size
								A  :	str				=					dropout
								A  :	Any				=					attention_dropout
								A  :	str				=					max_position_embeddings
								A  :	List[Any]				=					initializer_range
								A  :	Dict				=					scope
								A  :	Dict				=					bos_token_id





				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

								A  :	List[str]				=					ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size				)

								A  :	List[str]				=					None
								if self.use_input_mask:
												A  :	List[str]				=					random_attention_mask([self.batch_size, self.seq_length]				)

								if input_mask is not None:
												A  :	Any				=					input_mask.numpy()
												A  :	List[Any]				=					input_mask.shape
												A  :	int				=					np.random.randint(1       ,   seq_length - 1       ,   size=(batch_size,)				)
												for batch_idx, start_index in enumerate(snake_case_				):
																A  :	Optional[Any]				=					1
																A  :	int				=					0

								A  :	Dict				=					self.get_config()

								return config, input_ids, tf.convert_to_tensor(snake_case_				)





				def 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								return BlipTextConfig(
								    vocab_size=self.vocab_size       ,   hidden_size=self.hidden_size       ,   projection_dim=self.projection_dim       ,   num_hidden_layers=self.num_hidden_layers       ,   num_attention_heads=self.num_attention_heads       ,   intermediate_size=self.intermediate_size       ,   dropout=self.dropout       ,   attention_dropout=self.attention_dropout       ,   max_position_embeddings=self.max_position_embeddings       ,   initializer_range=self.initializer_range       ,   bos_token_id=self.bos_token_id       ,   )





				def 							_UpperCAmelCase    (		self			:		Optional[int]       ,   snake_case_			:		Optional[Any]       ,   snake_case_			:		Optional[int]       ,   snake_case_			:		List[str]				):


								"""simple docstring"""

								A  :	Union[str, Any]				=					TFBlipTextModel(config=snake_case_				)
								A  :	int				=					model(snake_case_       ,   attention_mask=snake_case_       ,   training=snake_case_				)
								A  :	Optional[int]				=					model(snake_case_       ,   training=snake_case_				)
								self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size)				)
								self.parent.assertEqual(result.pooler_output.shape       ,   (self.batch_size, self.hidden_size)				)






				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

								A  :	List[str]				=					self.prepare_config_and_inputs()
								A  :	List[Any]				=					config_and_inputs
								A  :	Any				=					{"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict



@require_tf
class      _SCREAMING_SNAKE_CASE				(		_UpperCamelCase,     unittest.TestCase				):
				lowerCamelCase_	  =  (TFBlipTextModel,) if is_tf_available() else ()
				lowerCamelCase_	  =  False
				lowerCamelCase_	  =  False
				lowerCamelCase_	  =  False





				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								A  :	Union[str, Any]				=					BlipTextModelTester(self				)
								A  :	List[Any]				=					ConfigTester(self       ,   config_class=snake_case_       ,   hidden_size=37				)





				def 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								self.config_tester.run_common_tests()





				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								A  :	int				=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case_				)





				def 							_UpperCAmelCase    (		self			:		List[str]				):


								"""simple docstring"""

								pass





				def 							_UpperCAmelCase    (		self			:		Union[str, Any]				):


								"""simple docstring"""

								pass





				@unittest.skip(reason='''Blip does not use inputs_embeds'''				)
				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

								pass





				@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''				)
				def 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								pass





				@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''				)
				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								pass





				@slow
				def 							_UpperCAmelCase    (		self			:		Dict				):


								"""simple docstring"""

								for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A  :	List[str]				=					TFBlipTextModel.from_pretrained(snake_case_				)
												self.assertIsNotNone(snake_case_				)






				def 							_UpperCAmelCase    (		self			:		Optional[Any]       ,   snake_case_			:		str=True				):


								"""simple docstring"""

								super().test_pt_tf_model_equivalence(allow_missing_keys=snake_case_				)






# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


SCREAMING_SNAKE_CASE__							:      List[str]         =    Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(4_2)

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    {"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

SCREAMING_SNAKE_CASE__							:      Any         =    """zero2"""
SCREAMING_SNAKE_CASE__							:      Dict         =    """zero3"""
SCREAMING_SNAKE_CASE__							:      str         =    [ZEROa, ZEROa]






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # customize the test name generator function as we want both params to appear in the sub-test
   # name, as by default it shows only the first param
   a__						:    Optional[Any]   		=	parameterized.to_safe_name("_".join(str(lowerCamelCase ) for x in param.args ) )
   return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 	__lowerCAmelCase      (       _UpperCamelCase	):





   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	Dict:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @require_torch_multi_gpu
   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	str:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   @require_torch_multi_gpu
   @parameterized.expand(snake_case       ,      name_func=snake_case   )
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      self.run_and_check(
          stage=snake_case       ,      model=snake_case       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      pass





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = 10       ,      snake_case = True       ,      snake_case = True       ,      snake_case = True       ,      )					->	str:
      """simple docstring"""





      a__						:    Tuple   		=	models[model]

      a__						:    int   		=	self.run_trainer(
          stage=snake_case       ,      model_name=snake_case       ,      eval_steps=snake_case       ,      num_train_epochs=1       ,      distributed=snake_case       ,      fpaa=snake_case       ,      )

      self.do_checks(snake_case   )

      return output_dir





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = 10       ,      snake_case = 1       ,      snake_case = True       ,      snake_case = True       ,      )					->	Optional[Any]:
      """simple docstring"""





      a__						:    str   		=	self.get_auto_remove_tmp_dir("./xxx"       ,      after=snake_case   )
      a__						:    List[Any]   		=	F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(snake_case   )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

      if fpaa:
         args.extend(["--fp16"]   )

      # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
      # hence the separate config files
      a__						:    str   		=	F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
      a__						:    Dict   		=	[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
      a__						:    Optional[int]   		=	self.get_launcher(snake_case   )

      a__						:    Optional[int]   		=	launcher + script + args + ds_args
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
      execute_subprocess_async(snake_case       ,      env=self.get_env()   )

      return output_dir





   def 						_snake_case			(			self       ,      snake_case=False   )					->	List[Any]:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	min(2       ,      get_gpu_count()   ) if distributed else 1
      return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()

