


'''simple docstring'''
import requests

__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		'''YOUR API KEY'''







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ = giphy_api_key ):
							A_			    =		"+".join(query.split() )
							A_			    =		f"https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"
							A_			    =		requests.get(UpperCamelCase_ ).json()["data"]
							return [gif["url"] for gif in gifs]


if __name__ == "__main__":
							print('''\n'''.join(get_gifs('''space ship''')))









'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__SCREAMING_SNAKE_CASE					:				Tuple									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_ ):
							if isinstance(UpperCamelCase_						,  np.ndarray ):
														return list(tensor.shape )

							A_			    =		tf.shape(UpperCamelCase_ )

							if tensor.shape == tf.TensorShape(UpperCamelCase_ ):
														return dynamic

							A_			    =		tensor.shape.as_list()

							return [dynamic[i] if s is None else s for i, s in enumerate(UpperCamelCase_ )]







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ = None						,  UpperCamelCase_ = None ):
							return tf.nn.softmax(logits=logits + 1e-9						,  axis=UpperCamelCase_						,  name=UpperCamelCase_ )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=1e-5						,  UpperCamelCase_=-1 ):
							# This is a very simplified functional layernorm, designed to duplicate
							# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
							# models in Transformers.

							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCamelCase_						,  UpperCamelCase_ ):
														raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis." )

							# Get mean and variance on the axis to be normalized
							A_    ,					A_			    =		tf.nn.moments(UpperCamelCase_						,  axes=[axis]						,  keepdims=UpperCamelCase_ )

							if axis != -1:
														# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
														# on every dimension except axis
														A_			    =		[1] * inputs.shape.rank
														A_			    =		shape_list(UpperCamelCase_ )[axis]
														A_			    =		tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )
														A_			    =		tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )

							# Compute layer normalization using the batch_normalization
							# function.
							A_			    =		tf.nn.batch_normalization(
							    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  offset=UpperCamelCase_						,  scale=UpperCamelCase_						,  variance_epsilon=UpperCamelCase_						,  )
							return outputs







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=0						,  UpperCamelCase_=-1 ):
							# Replicates the behavior of torch.flatten in TF

							# If end_dim or start_dim is negative, count them from the end
							if end_dim < 0:
														end_dim += input.shape.rank
							if start_dim < 0:
														start_dim += input.shape.rank

							if start_dim == end_dim:
														return input

							A_			    =		tf.shape(UpperCamelCase_ )
							A_			    =		tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
							A_			    =		tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]						,  axis=0 )
							return tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )







def       a_				(	UpperCamelCase_ ):
							if not isinstance(UpperCamelCase_						,  tf.Tensor ):
														A_			    =		tf.convert_to_tensor(UpperCamelCase_ )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														A_			    =		encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														A_			    =		encoder_attention_mask[:, None, None, :]
							# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
							# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
							# /transformer/transformer_layers.py#L270
							# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
							# encoder_extended_attention_mask.transpose(-1, -2))
							A_			    =		(
							    tf.cast(1						,  encoder_attention_mask.dtype ) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ = "input_ids" ):
							tf.debugging.assert_less(
							    UpperCamelCase_						,  tf.cast(UpperCamelCase_						,  dtype=tensor.dtype )						,  message=(
							        f"The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCamelCase_ )}) must be smaller than the embedding "
							        f"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
							    )						,  )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		6_4_5_1_2
							# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
							# because in that case even chunking the array would not make the saving
							# possible.
							A_			    =		[x for x in data if len(UpperCamelCase_ ) > HDF5_OBJECT_HEADER_LIMIT]

							# Expecting this to never be true.
							if bad_attributes:
														raise RuntimeError(
														    "The following attributes cannot be saved to HDF5 file because "
														    f"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
														    f"bytes: {bad_attributes}" )

							A_			    =		np.asarray(UpperCamelCase_ )

							A_			    =		1
							A_			    =		np.array_split(UpperCamelCase_						,  UpperCamelCase_ )

							# This will never loop forever thanks to the test above.
							while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
														num_chunks += 1
														A_			    =		np.array_split(UpperCamelCase_						,  UpperCamelCase_ )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(UpperCamelCase_ ):
																					A_			    =		chunk_data
							else:
														A_			    =		data







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							if name in group.attrs:
														A_			    =		[n.decode("utf8" ) if hasattr(UpperCamelCase_						,  "decode" ) else n for n in group.attrs[name]]
							else:
														A_			    =		[]
														A_			    =		0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode("utf8" ) if hasattr(UpperCamelCase_						,  "decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
																					chunk_id += 1
							return data







def       a_				(	UpperCamelCase_ ):

							def _expand_single_ad_tensor(UpperCamelCase_ ):
														if isinstance(UpperCamelCase_						,  tf.Tensor ) and t.shape.rank == 1:
																					return tf.expand_dims(UpperCamelCase_						,  axis=-1 )
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor						,  UpperCamelCase_ )





