
"""simple docstring"""




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase__      :    str    							= 5_0_0_0_0
UpperCAmelCase__      :    List[str]    							= 5_0_0_0

UpperCAmelCase__							,				UpperCAmelCase__      :    Optional[int]    							= os.path.split(__file__)
UpperCAmelCase__      :    Optional[Any]    							= os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def lowercase_  (   _snake_case   ,_snake_case						):
							for i in range(_snake_case						):
														SCREAMING_SNAKE_CASE__						:       Dict           =   dataset[i]

@get_duration
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							for i in range(0   ,len(_snake_case						)   ,_snake_case						):
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   dataset[i : i + batch_size]

@get_duration
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							with dataset.formatted_as(type=_snake_case						):
														for i in range(_snake_case						):
																					SCREAMING_SNAKE_CASE__						:       Dict           =   dataset[i]

@get_duration
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case						):
							with dataset.formatted_as(type=_snake_case						):
														for i in range(0   ,_snake_case   ,_snake_case						):
																					SCREAMING_SNAKE_CASE__						:       int           =   dataset[i : i + batch_size]

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   {"""num examples""": SPEED_TEST_N_EXAMPLES}
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [
							    (read, {"""length""": SMALL_TEST}),
							    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
							    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
							    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_000}),
							    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
							    (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
							    (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
							    (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
							    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
							    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_000}),
							]

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   [
							    (read, {"""length""": SMALL_TEST}),
							    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
							    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
							    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_000}),
							    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
							    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
							    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_000}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print("""generating dataset"""						)
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   datasets.Features(
														    {"""list""": datasets.Sequence(datasets.Value("""float32"""						)						), """numbers""": datasets.Value("""float32"""						)}						)
														SCREAMING_SNAKE_CASE__						:       str           =   generate_example_dataset(
														    os.path.join(_snake_case   ,"""dataset.arrow"""						)   ,_snake_case   ,num_examples=_snake_case   ,seq_shapes={"""list""": (100,)}   ,)
														print("""first set of iterations"""						)
														for func, kwargs in functions:
																					print(func.__name__   ,str(_snake_case						)						)
																					SCREAMING_SNAKE_CASE__						:       str           =   func(_snake_case   ,**_snake_case						)

														print("""shuffling dataset"""						)
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   dataset.shuffle()
														print("""Second set of iterations (after shuffling"""						)
														for func, kwargs in functions_shuffled:
																					print("""shuffled """   ,func.__name__   ,str(_snake_case						)						)
																					SCREAMING_SNAKE_CASE__						:       Tuple           =   func(
																					    _snake_case   ,**_snake_case						)

							with open(_snake_case   ,"""wb"""						) as f:
														f.write(json.dumps(_snake_case						).encode("""utf-8"""						)						)


if __name__ == "__main__":  # useful to run the profiler
					benchmark_iterating()











import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Tuple						=			"""M-CLIP"""







      def __init__(    self		,      lowerCAmelCase__=1_0_2_4		,      lowerCAmelCase__=7_6_8		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        a__	:					int										=transformerDimSize
        a__	:					Dict										=imageDimSize
        super().__init__(**lowerCAmelCase__							)







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Optional[Any]						=			MCLIPConfig







      def __init__(    self		,      lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					Tuple										=XLMRobertaModel(lowerCAmelCase__							)
        a__	:					List[str]										=torch.nn.Linear(
            in_features=config.transformerDimensions		,      out_features=config.numDims							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.transformer(input_ids=lowerCAmelCase__		,      attention_mask=lowerCAmelCase__							)[0]
        a__	:					int										=(embs * attention_mask.unsqueeze(2							)).sum(dim=1							) / attention_mask.sum(dim=1							)[:, None]
        return self.LinearTransformation(lowerCAmelCase__							), embs






